Dear Community,
In a RCT design with 2 groups (control group n=18 ; and Exercise Training group n=17) that were tested at baseline and at 2 months, I would like to know if:
1/ the proportion of participant reporting some symptoms such as "muscle pain" is significantly decreasing after the 2-month follow up in each group.
2/ if there is a significant difference on the proportion change between the group
For the question 1/, I am not sure if the Mc Nemar's test is the test I have to use
For the question 2/, I have no idea about the variable and the test that I can use
I give you here an exemple :
control group n=18 ; and Exercise Training group n=17.
In the control group : 11 participants are reporting muscle pain at baseline while they were 8 at the end of the study.
In the exercise training group : 11 participants are reporting muscle pain at baseline while they were 6 at the end of the study.
thank you for your precious insight
Merci
F
Wednesday, May 24, 2023
Interpretation Magnitude Interaction Two Continuous Variables Economic Significance
Hi all,
I have read a few threads regarding this topic (e.g. https://www.statalist.org/forums/for...uous-variables) but have not yet found an answer to my question. Apologies if the identical question has already been answered.
I have a count dependent variable (Y) (linear TWFE results are shown, but marginal effects from Poisson are virtually identical), which I have regressed on two continuous variables (X1 and X2) and their interaction.
The summary statistics of the variables are:
These are the results:
I am fully aware that the economic significance of a coefficient depends on the field, research question, etc. However, what is the methodology / logic to assess the economic significance of an interaction term between two continuous variables?
For instance, one often compares the magntiude of the coefficient on a dummy variable to the mean of the dependent variable to assess economic significance, and whether the effect is large enough to be "interesting". Similarly, what would one compare the coefficient on c.X1#c.X2 to in order to assess its magnitude?
Please let me know if my question is unclear, I would happy to rephrase it.
I have read a few threads regarding this topic (e.g. https://www.statalist.org/forums/for...uous-variables) but have not yet found an answer to my question. Apologies if the identical question has already been answered.
I have a count dependent variable (Y) (linear TWFE results are shown, but marginal effects from Poisson are virtually identical), which I have regressed on two continuous variables (X1 and X2) and their interaction.
The summary statistics of the variables are:
Code:
Variable | Obs Mean Std. dev. Min Max -------------+--------------------------------------------------------- Y | 373,763 .1933016 2.363418 0 502 X1 | 373,763 .5695304 .6594848 0 4.364 X2 | 373,763 -2246.553 773.906 -3651.53 316.896
Code:
HDFE Linear regression Number of obs = 373,763
Absorbing 3 HDFE groups F( 141, 7317) = 1955.95
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.0578
Adj R-squared = 0.0292
Within R-sq. = 0.0016
Number of clusters (token1) = 7,318 Root MSE = 2.3286
(Std. err. adjusted for 7,318 clusters in ID)
--------------------------------------------------------------------------------------------------------
| Robust
Y | Coefficient std. err. t P>|t| [95% conf. interval]
---------------------------------------+----------------------------------------------------------------
X1 | -.5015123 .1355351 -3.70 0.000 -.7672003 -.2358244
|
c.X1#c.X2 | -.0002419 .0000586 -4.13 0.000 -.0003567 -.000127
//The coefficient on X2 alone is perfectly collinear with the fixed-effectsFor instance, one often compares the magntiude of the coefficient on a dummy variable to the mean of the dependent variable to assess economic significance, and whether the effect is large enough to be "interesting". Similarly, what would one compare the coefficient on c.X1#c.X2 to in order to assess its magnitude?
Please let me know if my question is unclear, I would happy to rephrase it.
Thoughts about Cross-lagged panel
I am currently sitting on modeling for a paper and had actually assumed that I was well positioned with my model. However, in consultation with my supervisor, I now have doubts or doubts were sown. Since my supervisor has no idea at all about cross lagged models, I am looking for help here. My data consists of 3 waves in which I measure different constructs. Each of these constructs is used as a latent variable in my model. I have simplified my model here in the picture.
I have a set of questions, which form my independent latent construct X. I measured these, just like the mediator M and the dependent latent variable Y (set of questions), at 3 time points. I had previously read up on the topic of cross-lagged, however in such a model the relationship Y-->X would also be modeled, which is not supposed to be the case for me. Additionally, in a cross-lagged no effects within a wave are modeled.
My results are really very good for this model and fit my hypotheses. Nevertheless, my supervisor said that he has not seen the effects within a wave like this in a model and wonders if it can be done this way. The missing relation Y-->X is not a problem for him, but he still questions whether we are allowed to speak of a cross-lagged and whether the modeling can be done that way at all.
This would be my first question to the community. My second one goes in the direction that my supervisor said: If such a modeling is possible, then I would still have to use control variables such as age. Would this even be possible within the framework of this model. The interjection of a control variable is technically okay, but I don't know where I should insert it. The only thing that comes to mind is an additional mediator.
Thanks for your help in advance.
Christopher
Array
I have a set of questions, which form my independent latent construct X. I measured these, just like the mediator M and the dependent latent variable Y (set of questions), at 3 time points. I had previously read up on the topic of cross-lagged, however in such a model the relationship Y-->X would also be modeled, which is not supposed to be the case for me. Additionally, in a cross-lagged no effects within a wave are modeled.
My results are really very good for this model and fit my hypotheses. Nevertheless, my supervisor said that he has not seen the effects within a wave like this in a model and wonders if it can be done this way. The missing relation Y-->X is not a problem for him, but he still questions whether we are allowed to speak of a cross-lagged and whether the modeling can be done that way at all.
This would be my first question to the community. My second one goes in the direction that my supervisor said: If such a modeling is possible, then I would still have to use control variables such as age. Would this even be possible within the framework of this model. The interjection of a control variable is technically okay, but I don't know where I should insert it. The only thing that comes to mind is an additional mediator.
Thanks for your help in advance.
Christopher
Array
Tuesday, May 23, 2023
Order variables from left to right based on their latest value
I would like to order the variables (corresponding to the Stata command "order") from left to right based on the latest observation (here, the only observation of each variable that matters is the one corresponding to w_date=2022w46"). Here is the dataset:
In other words, I would like to automate the fact that I would like to order the variables as follow:
To give a bit of context, I have 100+ variables that are cumulative returns. What matters in my context is the highest generated return at the latest date of the sample, in this example 2022w46. I would like to see the one that generate the highest returns from left to right on the screen to select the later
Can you help me automate that?
Code:
clear all input str7 w_date cpd cpdpf cpdpm "2022w44" -.3522595 -.15837106 .45831277 "2022w45" -.05552628 .00728419 .63357966 "2022w46" -.04414876 .08082671 .65427268 end
In other words, I would like to automate the fact that I would like to order the variables as follow:
Code:
order w_date cpdpm cpdpf cpd
Can you help me automate that?
Rolling windows VAR IRF and graphs
Dear Stata users,
I am trying to implement the var, vector autoregression, and a panel var (pavar) stata command using rolling windows. While they do perfectly work, I'm not able to perform rolling impulse response function graphs or check stability and Granger causality under rolling windows.
The code I am using is
rolling, window(12) clear : pvar var1 var2 var3 vr4 var5 var6 lags(1)
pvarirf, step(12) impulse( var1 var2 var3s) response( vr4 var5 var6 ) cum oirf mc(2000)
However, I have difficulty when using the -rolling- prefix.
I would be grateful if you could point me in the right direction on how to obtain rolling impulse responses. Probably, it will need to be programed manually
Thank you so much for your time and consideration. I could provide data should that be necessary.
Best regards,
Mario
I am trying to implement the var, vector autoregression, and a panel var (pavar) stata command using rolling windows. While they do perfectly work, I'm not able to perform rolling impulse response function graphs or check stability and Granger causality under rolling windows.
The code I am using is
rolling, window(12) clear : pvar var1 var2 var3 vr4 var5 var6 lags(1)
pvarirf, step(12) impulse( var1 var2 var3s) response( vr4 var5 var6 ) cum oirf mc(2000)
However, I have difficulty when using the -rolling- prefix.
I would be grateful if you could point me in the right direction on how to obtain rolling impulse responses. Probably, it will need to be programed manually
Thank you so much for your time and consideration. I could provide data should that be necessary.
Best regards,
Mario
Repeated measures ANOVA
Dear Statalist,
I'm currently trying to replicate a piece of analysis originally done on SPSS in Stata 17 (Mac OS); specifically, a repeated-measures ANOVA.
The original SPSS code is:
The variables confrontT1 and confrontT3 are measures of intention to confront individuals who violate a social norm measured at time 1 and 3; changeconfrontCOVID is a control variable about perceptions a Covid-related norm.
The analysis was done on a dataset in wide format. I first reshaped the dataset into long format. The variables "confrontT1" and "confrontT3" are now "confrontT", "real_id" is an individual identifier, and "t" is time. The variable changeconfrontCOVID is a non-integer ranging from -100 to 100. A sample of the long format dataset is below,
Based on the Stata documentation, I believe the correct syntax should be:
However, I get the error message
If I remove the factor-variable operators and type real_id | changeconfrontCOVID, I get the following error message
I was wondering if you could tell me how to work around this issue. I would be grateful for any help.
Best regards,
Miguel Fonseca
I'm currently trying to replicate a piece of analysis originally done on SPSS in Stata 17 (Mac OS); specifically, a repeated-measures ANOVA.
The original SPSS code is:
Code:
GLM confrontT1 confrontT3 WITH changeconfrontCOVID /WSFACTOR=toename_confront_speed 2 Polynomial /METHOD=SSTYPE(3) /EMMEANS=TABLES(toename_confront_speed) WITH(changeconfrontCOVID=-30)COMPARE ADJ(LSD) /EMMEANS=TABLES(toename_confront_speed) WITH(changeconfrontCOVID=0)COMPARE ADJ(LSD) /EMMEANS=TABLES(toename_confront_speed) WITH(changeconfrontCOVID=30)COMPARE ADJ(LSD) /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /WSDESIGN=toename_confront_speed /DESIGN=changeconfrontCOVID.
The analysis was done on a dataset in wide format. I first reshaped the dataset into long format. The variables "confrontT1" and "confrontT3" are now "confrontT", "real_id" is an individual identifier, and "t" is time. The variable changeconfrontCOVID is a non-integer ranging from -100 to 100. A sample of the long format dataset is below,
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double confrontT float real_id byte t double changeconfrontCOVID 5 3 1 -29.5 4 3 3 -29.5 6 5 1 18 6 5 3 18 4.5 7 1 . 1 7 3 . 6 19 1 -7.5 7 19 3 -7.5 1.5 21 1 30 1.5 21 3 30 5 23 1 -65 3.5 23 3 -65 1 24 1 22.5 1 24 3 22.5 4 32 1 62 end
Code:
anova confrontT c.changeconfrontCOVID / i.real_id | c.changeconfrontCOVID t c.changeconfrontCOVID#t, repeat(t)
invalid interaction specification;
'|' requires a factor variable on each side
'|' requires a factor variable on each side
changeconfrontCOVID: factor variables may not contain noninteger values
Best regards,
Miguel Fonseca
Order data in descending order
Dear all,
I use the TNIC data from Hoberg&Phillips and want to sort the scores pro competitor_rank1 in descending order in order to keep only the top 5 competitors for each gvkey1. I am not experienced with STATA and tried to do it using the following code, but the score is still in descending order:
clear
cd "C:\Users\etc"
use tnic3
sort gvkey1 score
gen competitor_rank = _n
sort competitor_rank gvkey1
egen competitor_rank1 = group(gvkey1)
bysort competitor_rank1 (score): gen descending_order = _n
sort competitor_rank1 -descending_order
alternative:
use tnic3
sort gvkey1 score
gen competitor_rank= _n
sort competitor_rank gvkey1
egen competitor_rank1=group(gvkey1)
sort -score competitor_rank1
(here the error is "- invalid name" even if the column is names "score")
Any help, however small would be greatly appreciated! Thank you in advance!
I use the TNIC data from Hoberg&Phillips and want to sort the scores pro competitor_rank1 in descending order in order to keep only the top 5 competitors for each gvkey1. I am not experienced with STATA and tried to do it using the following code, but the score is still in descending order:
clear
cd "C:\Users\etc"
use tnic3
sort gvkey1 score
gen competitor_rank = _n
sort competitor_rank gvkey1
egen competitor_rank1 = group(gvkey1)
bysort competitor_rank1 (score): gen descending_order = _n
sort competitor_rank1 -descending_order
alternative:
use tnic3
sort gvkey1 score
gen competitor_rank= _n
sort competitor_rank gvkey1
egen competitor_rank1=group(gvkey1)
sort -score competitor_rank1
(here the error is "- invalid name" even if the column is names "score")
Any help, however small would be greatly appreciated! Thank you in advance!
generating many dummy variables with the var name and label name
Hi all,
Although I was going through some of the website links, I could not solve my issue. So, I am posting here and seeking the help. My issue is that I need to generate manynew dummy varaibles out of a categorical variable. For example the variable name is districtname_main which is a string type and it has 32 districts (See the attached picture). I have to gen all of them into new binary dummy variables with the var name and label name of that particular district. So I tried the following command but did not work.
foreach var of districtname_main {
gen dist_`var'=0
replace dist_`var'=1 if districtname_main ==real("`var'")
}
I also tried many other methods suggested on various websites. But nothing worked. I wonder if someone could help me out from this issue.
Although I was going through some of the website links, I could not solve my issue. So, I am posting here and seeking the help. My issue is that I need to generate manynew dummy varaibles out of a categorical variable. For example the variable name is districtname_main which is a string type and it has 32 districts (See the attached picture). I have to gen all of them into new binary dummy variables with the var name and label name of that particular district. So I tried the following command but did not work.
foreach var of districtname_main {
gen dist_`var'=0
replace dist_`var'=1 if districtname_main ==real("`var'")
}
I also tried many other methods suggested on various websites. But nothing worked. I wonder if someone could help me out from this issue.
Normalize variable as an expanding window
Hi,
I would like to normalize the price of the following as an expanding window taking into account all information up to time t (but not after t, to avoid look-ahead bias). So far, I have the code that takes into account the entire dataset, and hence, induces a look-ahead bias. Can someone help me edit the code? Thanks
I would like to normalize the price of the following as an expanding window taking into account all information up to time t (but not after t, to avoid look-ahead bias). So far, I have the code that takes into account the entire dataset, and hence, induces a look-ahead bias. Can someone help me edit the code? Thanks
Code:
clear
local ticker "BTC-USD"
getsymbols `ticker', fm(1) fd(1) fy(2012) lm(12) frequency(d) price(adjclose) yahoo clear
keep period p_adjclose_BTC_USD
* Normalization:
local To_Norm p_adjclose_BTC_USD
foreach var in `To_Norm' {
sum `var', meanonly
gen n_`var' = (`var' - r(min)) / (r(max) - r(min))
}Monday, May 22, 2023
How do I find out which excel file was imported into STATA (origin of data?)
Hey all,
Months ago, I imported an excel file into stata for analysis. However, I would like to do this again but in a new dta but I cannot remember which excel file I imported into stata. Is there a way to find this out through history?
Thank you,
Rajiv
Months ago, I imported an excel file into stata for analysis. However, I would like to do this again but in a new dta but I cannot remember which excel file I imported into stata. Is there a way to find this out through history?
Thank you,
Rajiv
Query on ordering coefficients from multiple models when using coefplot
Hi All
We need help with using the coefplot command. Briefly, we have run a series logistic regression models (5 models in total with a loop to make it easier). These regression models also include interactions terms between two categorical variables (ACEscore and sexual). This is followed by 'margins' to obtain predicted probabilities of the interaction categories/groups:
We then use 'coefplot' to plot just the predicted probabilities in a figure:
By default, coefplot orders the estimates/margins in the figure (see attached) according to the 'ACEscore' variables' categories. However, we would like to order the margins by model (kesscat docdep selfharm suicide victim). It doesn't seem like the order function is helpful here (unless if we're doing something wrong!).
Any tips would be really helpful!
Many thanks
/Amal
We need help with using the coefplot command. Briefly, we have run a series logistic regression models (5 models in total with a loop to make it easier). These regression models also include interactions terms between two categorical variables (ACEscore and sexual). This is followed by 'margins' to obtain predicted probabilities of the interaction categories/groups:
Code:
foreach var of varlist kesscat docdep selfharm suicide victim {
svy: logit `var' i.ACEscore##i.sexual
eststo `var': margins i.ACEscore#i.sexual, post
}Code:
coefplot kesscat docdep selfharm suicide victim
Any tips would be really helpful!
Many thanks
/Amal
Reminder: UK Stata Conference submission deadline 26 May
UK Stata Conference, 7-8 September 2023: reminder
I'm bumping the thread at https://www.statalist.org/forums/for...and-first-call to remind you of the upcoming submission deadline.
Please follow that link for information about how to submit. Registration information will be coming shortly.
The headline new information is that the venue has had to be changed from UCL (where we were last year) to the LSE's Marshall Building -- further details to come. (As an LSE faculty member, I assure you that it's a great venue -- brand new -- and very conveniently located. See :https://www.lse.ac.uk/lse-information/campus-map)
We look forward to hearing from you,
Stephen (and Tim and Roger)
I'm bumping the thread at https://www.statalist.org/forums/for...and-first-call to remind you of the upcoming submission deadline.
Please follow that link for information about how to submit. Registration information will be coming shortly.
The headline new information is that the venue has had to be changed from UCL (where we were last year) to the LSE's Marshall Building -- further details to come. (As an LSE faculty member, I assure you that it's a great venue -- brand new -- and very conveniently located. See :https://www.lse.ac.uk/lse-information/campus-map)
We look forward to hearing from you,
Stephen (and Tim and Roger)
Stata Command: including interaction terms of the endogenous variable in 2SLS using xtivreg
Hi,
I am wondering if anyone had any experience in including an interaction term in 2SLS using xtivreg?
I am now having a successful 2SLS, not including any interaction terms as follows:
inverse_D is the endogenous variable and toughness is the instrumental variable
This is successful, and I have the results like this:
And now, I want to add an interaction term: log_analysts. I found that in previous posts that integrating interaction terms into a new variable is recommended, so I did:
And then I suppose that my code should be:
I wonder if this is correct. In particular, I want to know if variable 'log_analysts' should exist outside the parenthesis, and more importantly: does this mean that I am using one instrumental variable 'toughness' on one endogenous variable 'inverse_D'? I was worried that this implies using 2 instruments on 2 endogenous variables, with 2x2 = 4 first stage estimations.
For more information, I am trying to mimic the code using ivreg2:
where w is the interaction term variable, x is the endogenous variable and z is the instrument. I don't know if it will be the same in xtivreg though. Any other suggestions or recommendations will be much appreciated.
Many Thanks,
Harry
I am wondering if anyone had any experience in including an interaction term in 2SLS using xtivreg?
I am now having a successful 2SLS, not including any interaction terms as follows:
Code:
xtivreg PatMV_real_log Lev CAPX_AT Size rd_log sale_log number_log (inverse_D = toughness_normalized) i.fyear, fe
This is successful, and I have the results like this:
Code:
Fixed-effects (within) IV regression Number of obs = 5,066
Group variable: all_cluster Number of groups = 554
R-squared: Obs per group:
Within = . min = 1
Between = 0.2823 avg = 9.1
Overall = 0.3891 max = 31
Wald chi2(37) = 19922.00
corr(u_i, Xb) = 0.0856 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
PatMV_real~g | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
inverse_D | 13.87709 5.667382 2.45 0.014 2.769226 24.98496
Lev | .0032969 .0029638 1.11 0.266 -.002512 .0091058
CAPX_AT | 1.526791 .6927957 2.20 0.028 .1689361 2.884645
Size | .0862929 .0621718 1.39 0.165 -.0355617 .2081474
rd_log | -.084102 .3179503 -0.26 0.791 -.7072731 .539069
sale_log | .1517564 .0322944 4.70 0.000 .0884605 .2150523
number_log | .0404435 .0334691 1.21 0.227 -.0251548 .1060417
|
fyear |
1986 | -.1775167 .298694 -0.59 0.552 -.7629462 .4079129
1987 | -.0519888 .3001776 -0.17 0.862 -.6403262 .5363485
1988 | -.0481431 .3076404 -0.16 0.876 -.6511073 .554821
1989 | .0163748 .2844115 0.06 0.954 -.5410614 .573811
1990 | -.0718744 .2990964 -0.24 0.810 -.6580925 .5143437
1991 | -.2075746 .3433348 -0.60 0.545 -.8804984 .4653492
1992 | -.2268283 .3502615 -0.65 0.517 -.9133282 .4596715
1993 | .1302594 .3597807 0.36 0.717 -.5748978 .8354166
1994 | .394627 .391701 1.01 0.314 -.3730928 1.162347
1995 | -.2333602 .4519216 -0.52 0.606 -1.11911 .6523898
1996 | .1275317 .4482286 0.28 0.776 -.7509803 1.006044
1997 | -.1892146 .55809 -0.34 0.735 -1.283051 .9046217
1998 | -.3070482 .5836953 -0.53 0.599 -1.45107 .8369736
1999 | -.6370051 .6263369 -1.02 0.309 -1.864603 .5905927
2000 | -.8418151 .6641659 -1.27 0.205 -2.143556 .4599261
2001 | -1.119607 .7252079 -1.54 0.123 -2.540988 .3017744
2002 | -1.545625 .8150542 -1.90 0.058 -3.143102 .0518518
2003 | -1.636655 .8022847 -2.04 0.041 -3.209104 -.0642054
2004 | -1.786408 .8443835 -2.12 0.034 -3.441369 -.1314466
2005 | -1.878826 .8648506 -2.17 0.030 -3.573902 -.1837496
2006 | -2.023935 .8749888 -2.31 0.021 -3.738882 -.3089884
2007 | -2.223262 .9041965 -2.46 0.014 -3.995455 -.4510695
2008 | -2.062127 .8859147 -2.33 0.020 -3.798488 -.3257664
2009 | -2.24123 .9245712 -2.42 0.015 -4.053357 -.4291043
2010 | -2.249556 .9657848 -2.33 0.020 -4.142459 -.3566523
2011 | -2.138431 .923046 -2.32 0.021 -3.947568 -.3292945
2012 | -2.460573 .9528901 -2.58 0.010 -4.328203 -.592943
2013 | -3.264231 .9082721 -3.59 0.000 -5.044412 -1.484051
2014 | -4.738765 .8172058 -5.80 0.000 -6.340459 -3.137071
2015 | -7.135273 .62943 -11.34 0.000 -8.368933 -5.901613
|
_cons | 1.398011 .499853 2.80 0.005 .4183173 2.377705
-------------+----------------------------------------------------------------
sigma_u | 1.6160709
sigma_e | 1.3406031
rho | .5923664 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(553,4475) = 4.74 Prob > F = 0.0000
------------------------------------------------------------------------------
Instrumented: inverse_D
Instruments: Lev CAPX_AT Size rd_log sale_log number_log 1986.fyear
1987.fyear 1988.fyear 1989.fyear 1990.fyear 1991.fyear
1992.fyear 1993.fyear 1994.fyear 1995.fyear 1996.fyear
1997.fyear 1998.fyear 1999.fyear 2000.fyear 2001.fyear
2002.fyear 2003.fyear 2004.fyear 2005.fyear 2006.fyear
2007.fyear 2008.fyear 2009.fyear 2010.fyear 2011.fyear
2012.fyear 2013.fyear 2014.fyear 2015.fyear toughness_normalizedCode:
gen int_log_analysts = c.inverse_D#c.log_analysts gen int_log_analysts_iv = c.toughness_normalized#c.log_analysts
Code:
xtivreg PatMV_real_log Lev CAPX_AT Size rd_log sale_log number_log log_analysts (inverse_D int_log_analysts= toughness_normalized int_log_analysts_iv) i.fyear, fe
For more information, I am trying to mimic the code using ivreg2:
Code:
ivreg2 y w (x c.x#c.w= z c.z#c.w)
Many Thanks,
Harry
Sunday, May 21, 2023
number increased or decreased?
Dear All, I found this question here (in Chinese). The variable x denotes the product.
The question is to obtain (1) the number of new added product, and (2) the number of retired product, compared to the situation in the previous year for each firm (id) and each year. Any suggestions are highly appreciated.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input id year str1 x 1 2011 "a" 1 2011 "b" 1 2012 "a" 1 2012 "d" 1 2013 "a" 1 2013 "d" 1 2013 "c" 2 2011 "c" 2 2011 "d" 2 2012 "c" 2 2012 "a" 2 2013 "c" 2 2013 "b" end
inter-religious marriage
Dear sir
I am working with Census data. To make it simple, consider a database as follows:
I need to count the number of same-religion and different-religion marriages.
In this very simple database above, the result should be: AA = 1; AB = 1; BA = 1.
So far I managed to create a new variable "position-religion" (1-A; 2-A; 3-A; 1A; 2-B; ...).
I guess I have to create another new variable, assigning the position_religion of the head of the family to all the other ids in the same family_ids. If I manage to do that, a simple frequency table will provide the result.
Could you please help me in creating this new variable?
Thanks in advance
Sergio Goldbaum
I am working with Census data. To make it simple, consider a database as follows:
| id | family_id | position (in family) | religion |
| 1 | 1 | head (1) | A |
| 2 | 1 | partner (2) | A |
| 3 | 1 | son (3) | A |
| 4 | 2 | head (1) | A |
| 5 | 2 | partner (2) | B |
| 6 | 3 | head (1) | A |
| 7 | 4 | head (1) | B |
| 8 | 4 | partner (2) | A |
| 9 | 4 | son (3) | A |
In this very simple database above, the result should be: AA = 1; AB = 1; BA = 1.
So far I managed to create a new variable "position-religion" (1-A; 2-A; 3-A; 1A; 2-B; ...).
I guess I have to create another new variable, assigning the position_religion of the head of the family to all the other ids in the same family_ids. If I manage to do that, a simple frequency table will provide the result.
Could you please help me in creating this new variable?
Thanks in advance
Sergio Goldbaum
Problem with csdid command
I have the following dataset:
I am trying to obtain a did estimator a la Callaway and Sant'anna running the following command:
However, I obtain the following result:
I would appreciate if FernandoRios could help me.
Code:
id_municipio ano uf munic imposto_renda taxpayers ano_3g ano_4g treatment_3g 1200013 2013 AC Acrelândia 430014 128 2011 2018 2011 1200054 2013 AC Assis Brasil 205203 97 2015 2021 0 1200104 2013 AC Brasiléia 1708153 361 2015 2017 0 1200138 2013 AC Bujari 243841 89 2010 2017 2010 1200179 2013 AC Capixaba 236769 102 2015 2019 0 1200203 2013 AC Cruzeiro do Sul 8797109 1031 2008 2016 2008 1200252 2013 AC Epitaciolândia 2199140 252 2013 2017 2013 1200302 2013 AC Feijó 447522 282 2015 2017 0 1200328 2013 AC Jordão 36289 35 2016 2020 0 1200336 2013 AC Mâncio Lima 597157 91 2011 2017 2011 1200344 2013 AC Manoel Urbano 105124 66 2016 2021 0 1200351 2013 AC Marechal Thaumaturgo 47086 20 2016 2020 0 1200385 2013 AC Plácido de Castro 262876 146 2015 2019 0 1200807 2013 AC Porto Acre 145717 87 2014 2018 0 1200393 2013 AC Porto Walter 23606 15 2016 2021 0 1200401 2013 AC Rio Branco 1.594e+08 10252 2008 2014 2008 1200427 2013 AC Rodrigues Alves 54578 63 2015 2018 0 1200435 2013 AC Santa Rosa do Purus 51197 34 2016 2020 0 1200500 2013 AC Sena Madureira 1006259 347 2015 2017 0 1200450 2013 AC Senador Guiomard 1706722 327 2011 2017 2011
Code:
clear import delimited "C:\Users\mateu\OneDrive\Documentos\base_tax.csv" keep if ano >= 2004 & ano <= 2013 generate treatment_3g = 0 replace treatment_3g = ano_3g if ano >= ano_3g xtset id_municipio ano csdid imposto_renda , ivar(id_municipio) time(ano) gvar(treatment_3g) method(dripw)
Code:
csdid imposto_renda , ivar(id_municipio) time(ano) gvar(treatment_3g) method(dripw)
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxx
Difference-in-difference with Multiple Time Periods
Number of obs = 0
Outcome model : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
g2008 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2007_2009 | 0 (omitted)
t_2007_2010 | 0 (omitted)
t_2007_2011 | 0 (omitted)
t_2007_2012 | 0 (omitted)
t_2007_2013 | 0 (omitted)
-------------+----------------------------------------------------------------
g2009 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2008_2009 | 0 (omitted)
t_2008_2010 | 0 (omitted)
t_2008_2011 | 0 (omitted)
t_2008_2012 | 0 (omitted)
t_2008_2013 | 0 (omitted)
-------------+----------------------------------------------------------------
g2010 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2008_2009 | 0 (omitted)
t_2009_2010 | 0 (omitted)
t_2009_2011 | 0 (omitted)
t_2009_2012 | 0 (omitted)
t_2009_2013 | 0 (omitted)
-------------+----------------------------------------------------------------
g2011 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2008_2009 | 0 (omitted)
t_2009_2010 | 0 (omitted)
t_2010_2011 | 0 (omitted)
t_2010_2012 | 0 (omitted)
t_2010_2013 | 0 (omitted)
-------------+----------------------------------------------------------------
g2012 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2008_2009 | 0 (omitted)
t_2009_2010 | 0 (omitted)
t_2010_2011 | 0 (omitted)
t_2011_2012 | 0 (omitted)
t_2011_2013 | 0 (omitted)
-------------+----------------------------------------------------------------
g2013 |
t_2004_2005 | 0 (omitted)
t_2005_2006 | 0 (omitted)
t_2006_2007 | 0 (omitted)
t_2007_2008 | 0 (omitted)
t_2008_2009 | 0 (omitted)
t_2009_2010 | 0 (omitted)
t_2010_2011 | 0 (omitted)
t_2011_2012 | 0 (omitted)
t_2012_2013 | 0 (omitted)
------------------------------------------------------------------------------
Control: Never Treated
See Callaway and Sant'Anna (2021) for details
.
end of do-fileHausman test after reghdfe with two-way cluster
Hello,
I am a complete novice in terms of Stata and have encountered a challenge I can’t seem to overcome. I have tried searching the forum (and the web) for answers but haven’t found one that lets me overcome the challenge.
I have a two-way fixed effects model with two-way clustering using reghdfe on panel data with T = 10 and N = 423. To test the use of FE I would like to run a Hausman test. However, I can't seem to figure out how to run a Hausman test with two-way clustering, nor am I sure how to run an equivalent model with RE since I am using reghdfe.
I am a complete novice in terms of Stata and have encountered a challenge I can’t seem to overcome. I have tried searching the forum (and the web) for answers but haven’t found one that lets me overcome the challenge.
I have a two-way fixed effects model with two-way clustering using reghdfe on panel data with T = 10 and N = 423. To test the use of FE I would like to run a Hausman test. However, I can't seem to figure out how to run a Hausman test with two-way clustering, nor am I sure how to run an equivalent model with RE since I am using reghdfe.
Code:
. reghdfe BVLEV_1 L.INNO L.SIZE L.AGE L.TANG L.PROF L.GRTH L.NDTS L.MrktD c.L.INNO#i.L.MrktD, absorb(Year FIRM) vce(cluster Year FIRM)
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(BVLEV_1 INNO) double(SIZE AGE) float(TANG PROF GRTH NDTS MrktD)
. 0 25.140104442084407 4.736198448394496 .2805809 .1225458 1.728044 0 1
1.0106446 0 25.271209371482893 4.74493212836325 .26030242 .102776 1.0134124 0 1
.9059817 .6931472 25.31613062108747 4.7535901911063645 .24522354 .12964672 1.2477313 0 1
1.0244187 0 25.199010475505425 4.762173934797756 .2690079 .09200912 1.211202 0 1
.9241343 0 25.212149367154357 4.770684624465665 .25968078 .09518524 .9013919 0 1
.9028375 0 25.177729405521426 4.77912349311153 .2600798 .07181802 .9193362 0 1
.8666971 0 25.12733523500807 4.787491742782046 .25805798 .1064541 1.3683108 0 1
.7276743 0 25.279035672649883 4.795790545596741 .2284465 .1694506 1.6841967 0 1
.6681173 0 25.342871330740614 4.804021044733257 .21429476 .15791163 1.3381064 0 1
.3958571 0 25.371792403193993 4.812184355372417 .23927324 .11115817 1.897365 0 1
. 0 25.533537795756093 4.736198448394496 .07599856 .07023368 .7052656 0 1
1.1333276 0 25.54093107974409 4.74493212836325 .08478917 .1016431 .56339264 0 1
1.2531534 0 25.59322043341546 4.7535901911063645 .0899643 .04553749 .4952209 0 1
1.2157818 0 25.646176772676757 4.762173934797756 .08490727 .06337555 .6159959 0 1
1.2367824 0 25.69303746899461 4.770684624465665 .0767672 .05830297 .7429931 0 1
1.2092685 0 25.762287725487898 4.77912349311153 .06659363 .06440251 .6952531 0 1
1.0835882 0 25.717032993922302 4.787491742782046 .06419417 .06779025 .830492 0 1
1.2075226 0 25.79551036601393 4.795790545596741 .062812395 .04183229 .6352618 0 1
1.202427 0 25.87415570568361 4.804021044733257 .06573743 .04855713 .4949357 0 1
1.1884935 0 25.879080258005892 4.812184355372417 .10492945 .05894396 .6899122 0 1
. 5.755742 24.196535787431234 4.736198448394496 .14523625 .08608548 1.1286103 0 1
.6924891 6.234411 24.136589398439337 4.74493212836325 .13476273 .05329347 .6220146 0 1
.7580355 5.958425 24.152208078949545 4.7535901911063645 .1254282 .05762918 .8005841 0 1
.7836558 5.720312 24.13471053509311 4.762173934797756 .13485539 .0600852 .8276075 0 1
.7642021 6.570883 24.215400213631103 4.770684624465665 .15209243 .05421892 1.1268517 0 1
.7537746 6.526495 24.3121315669416 4.77912349311153 .1557181 .09528464 1.0803771 0 1
.7622334 6.122493 24.306479173183142 4.787491742782046 .1659288 .0975802 1.2334186 0 1
.7618092 6.113682 24.39860402051902 4.795790545596741 .16392794 .10700773 1.2668626 0 1
.8007717 5.940171 24.440441160239498 4.804021044733257 .1601782 .05361722 .9788643 0 1
.8087863 4.867535 24.472299213903636 4.812184355372417 0 .0878969 1.0270094 0 1
. 0 21.31712734644807 4.7535901911063645 .6161835 .035405505 .4728191 .0820877 0
.4618512 0 21.337435284753997 4.762173934797756 .55176055 .04723222 .56485796 .07206534 0
.5941403 0 21.36078455087655 4.770684624465665 .6694127 .05526852 .5964243 .04811 0
.6679801 0 21.4577257720642 4.77912349311153 .60712 .08645748 .5824009 .04505779 0
. 0 23.695985336242494 4.736198448394496 .7190117 .04887533 .553095 .03741924 1
.7705187 0 23.86249221018245 4.74493212836325 .7456001 .14748636 .4454397 .033855498 1
.682672 1.609438 23.65903576938081 4.7535901911063645 .3385791 .04103007 .43538275 .035442423 1
.6286231 2.484907 23.587521747769042 4.762173934797756 .3293337 .029031644 .5320368 .03727587 1
.59591955 2.564949 23.578585956841195 4.770684624465665 .3091892 .03524181 .6112404 .034720317 1
.57921785 0 23.57379891306601 4.77912349311153 .2910932 .021688854 .6228262 .05062613 1
.5702116 1.0986123 23.59059639575273 4.787491742782046 .26903787 .05534378 .7834185 .02917658 1
.5314447 1.94591 23.58866160882936 4.795790545596741 .2601817 0 1.0465562 .02840274 1
.5513356 .6931472 23.624852252665107 4.804021044733257 .2459092 .06453186 .7979235 .02741656 1
.4822459 0 24.043795315577245 4.812184355372417 .8499157 .1873104 .7865514 .02106505 1
. 0 22.28098931390445 4.770684624465665 .3840807 .09461883 .8451321 .04506727 1
.6773132 0 22.42692242480151 4.77912349311153 .3791458 .12221717 1.1784273 .04725125 1
.9319066 3.2580965 22.698318613027467 4.787491742782046 .4210063 .07018868 .8177284 .033333335 1
.8060787 2.772589 23.159944664508608 4.795790545596741 .3842118 .1145391 .7923383 .04621534 1
.8309887 1.3862944 23.28126804188242 4.804021044733257 .4161632 .10714693 .6933689 .04023709 1
.908798 .6931472 23.206337470872167 4.812184355372417 .48673666 .079771 .7126593 .04398855 1
. 0 24.037888110112725 4.663439094112067 .20535256 .07454053 .7056163 0 1
.78619 0 24.107147496432795 4.672828834461906 .20766094 .0847304 .5626268 0 1
.759084 0 23.7972083710832 4.68213122712422 .1808103 .0906814 .8042296 0 1
.6937635 0 23.81739443240191 4.6913478822291435 .18839782 .09374084 1.2695315 0 1
.7142321 0 23.8684520637118 4.700480365792417 .18411104 .09293253 1.0812703 0 1
.7059544 0 23.980323373893167 4.709530201312334 .1874382 .1009305 1.300577 0 1
.7414396 0 24.046249393101697 4.718498871295094 .19512346 .064213924 1.0034713 0 1
.6974292 0 24.196225584866482 4.727387818712341 .19427302 .08273678 1.0555807 0 1
.6635369 0 24.272739535271963 4.736198448394496 .20506677 .08730604 .7278484 0 1
.7489944 0 24.335264246751386 4.74493212836325 .23616904 .0170746 .8092557 0 1
. 6.767343 24.84321316656426 4.6443908991413725 .23399247 .1530494 1.5778688 0 1
.6145601 6.999423 24.929092141846183 4.653960350157523 .22023107 .16188905 1.1156516 0 1
.8525485 7.23201 24.901093336322354 4.663439094112067 .2153826 .12069391 1.2227247 0 1
.6608409 7.525101 24.884739312357365 4.672828834461906 .1985463 .05202068 1.0817511 0 1
.6709611 7.751045 24.99147966916149 4.68213122712422 .18963976 .09555482 .9194262 0 1
.6702911 7.624131 25.054709451257363 4.6913478822291435 .19192806 .08739167 .7840677 0 1
.656939 6.656726 25.01080320478844 4.700480365792417 .18766014 .08970646 .9115818 0 1
.8626414 6.364751 25.07917947577587 4.709530201312334 .19410613 .1058089 1.0186787 0 1
.6024605 5.986452 25.19152750567851 4.718498871295094 .19143543 .12674797 .7005265 0 1
.8392656 5.631212 25.18092160765754 4.727387818712341 .2275152 .0998321 .9155501 0 1
. 0 21.286085060398946 4.634728988229636 .18472242 -.03785776 .26592264 0 1
.799298 0 21.361346297431304 4.6443908991413725 .1597743 .05477314 .1627046 0 1
.8547568 0 21.417737140967827 4.653960350157523 .1535219 .05243392 .216123 0 1
.8669059 0 21.487178517425455 4.663439094112067 .1421967 .05316325 .2241691 0 1
.9250264 0 22.04845471091829 4.672828834461906 .10988519 .04891023 .2852529 0 1
.8776872 0 22.17889431155691 4.68213122712422 .09379284 .08198924 .5102885 0 1
.9845775 0 22.577186002682694 4.6913478822291435 .118811 .050721 .55521816 0 1
1.0301828 0 22.966175232858753 4.700480365792417 .11179797 .04159862 .3913315 0 1
.9712481 0 23.10771659050218 4.709530201312334 .10200204 .0592987 .3879843 0 1
1.0335312 0 23.14959836545931 4.718498871295094 .067471266 .03896626 .333397 0 1
. 4.875197 25.389907345032007 4.61512051684126 .19899076 .07385645 .7468041 0 1
.8884309 4.990433 25.346550952369494 4.624972813284271 .244148 .0394978 .4098155 0 1
1.0635477 5.257495 25.424400534081013 4.634728988229636 .2192063 .05449627 .640307 0 1
1.1581262 5.521461 25.41631868691919 4.6443908991413725 .2271549 .0207892 .6342526 0 1
.9200982 5.416101 25.44460887405928 4.653960350157523 .22096443 .04179115 .7652075 0 1
.9452122 4.4426513 25.540138372607856 4.663439094112067 .22103485 .032837752 .7096379 0 1
.9826649 4.4998097 25.521623333047007 4.672828834461906 .21811807 .073082656 .7592581 0 1
.94481 4.1743875 25.530462163010963 4.68213122712422 .21400774 .08259459 .8449045 0 1
.9603899 2.833213 25.546004059923572 4.6913478822291435 .21670502 .05456676 .5521333 0 1
1.0113537 2.0794415 25.51107419627515 4.700480365792417 .2304509 .029857313 .6177271 0 1
. .6931472 26.30204804427632 4.564348191467836 .23235023 .05660253 .7365565 0 1
1.0989978 0 26.461021305173926 4.574710978503383 .22211842 .0761485 .4265231 0 1
1.0847838 0 26.439131679448664 4.584967478670572 .2480531 .0520219 .5241512 0 1
1.1244308 0 26.33223200982412 4.59511985013459 .22592357 .020700116 .4890538 0 1
1.299305 0 26.368868199539683 4.605170185988092 .22564612 .015210397 .4428461 0 1
1.0879376 0 26.482558701533332 4.61512051684126 .23024334 0 .42624265 0 1
1.0736886 0 26.434295321811224 4.624972813284271 .22703527 .05220648 .53496367 0 1
1.0511376 2.0794415 26.541242693517702 4.634728988229636 .21943107 .07352107 .7395294 0 1
1.1086005 0 26.69660714579725 4.6443908991413725 .2080971 .0726368 .4884022 0 1
1.0815631 0 26.79659604452628 4.653960350157523 .18448013 .09437406 .6085445 0 1
endHausman test after reghdfe with two-way cluster
Hello,
I am a complete novice in terms of Stata and have encountered a challenge I can’t seem to overcome. I have tried searching the forum (and web) for answers but haven’t found one that lets me overcome the challenge.
I have a two-way fixed effects model with two-way clustering using reghdfe on panel data with T = 10 and N = 423. To test the use of FE I would like to run a Hausman test. However, I can't seem to figure out how to run a Hausman test with two-way clustering, nor am I sure how to run a equivalent model with RE since I am using reghdfe.
I am a complete novice in terms of Stata and have encountered a challenge I can’t seem to overcome. I have tried searching the forum (and web) for answers but haven’t found one that lets me overcome the challenge.
I have a two-way fixed effects model with two-way clustering using reghdfe on panel data with T = 10 and N = 423. To test the use of FE I would like to run a Hausman test. However, I can't seem to figure out how to run a Hausman test with two-way clustering, nor am I sure how to run a equivalent model with RE since I am using reghdfe.
Code:
. reghdfe BVLEV_1 L.INNO L.SIZE L.AGE L.TANG L.PROF L.GRTH L.NDTS L.MrktD c.L.INNO#i.L.MrktD, absorb(Year FIRM) vce(cluster Year FIRM)
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(BVLEV_1 INNO) double(SIZE AGE) float(TANG PROF GRTH NDTS MrktD)
. 0 25.140104442084407 4.736198448394496 .2805809 .1225458 1.728044 0 1
1.0106446 0 25.271209371482893 4.74493212836325 .26030242 .102776 1.0134124 0 1
.9059817 .6931472 25.31613062108747 4.7535901911063645 .24522354 .12964672 1.2477313 0 1
1.0244187 0 25.199010475505425 4.762173934797756 .2690079 .09200912 1.211202 0 1
.9241343 0 25.212149367154357 4.770684624465665 .25968078 .09518524 .9013919 0 1
.9028375 0 25.177729405521426 4.77912349311153 .2600798 .07181802 .9193362 0 1
.8666971 0 25.12733523500807 4.787491742782046 .25805798 .1064541 1.3683108 0 1
.7276743 0 25.279035672649883 4.795790545596741 .2284465 .1694506 1.6841967 0 1
.6681173 0 25.342871330740614 4.804021044733257 .21429476 .15791163 1.3381064 0 1
.3958571 0 25.371792403193993 4.812184355372417 .23927324 .11115817 1.897365 0 1
. 0 25.533537795756093 4.736198448394496 .07599856 .07023368 .7052656 0 1
1.1333276 0 25.54093107974409 4.74493212836325 .08478917 .1016431 .56339264 0 1
1.2531534 0 25.59322043341546 4.7535901911063645 .0899643 .04553749 .4952209 0 1
1.2157818 0 25.646176772676757 4.762173934797756 .08490727 .06337555 .6159959 0 1
1.2367824 0 25.69303746899461 4.770684624465665 .0767672 .05830297 .7429931 0 1
1.2092685 0 25.762287725487898 4.77912349311153 .06659363 .06440251 .6952531 0 1
1.0835882 0 25.717032993922302 4.787491742782046 .06419417 .06779025 .830492 0 1
1.2075226 0 25.79551036601393 4.795790545596741 .062812395 .04183229 .6352618 0 1
1.202427 0 25.87415570568361 4.804021044733257 .06573743 .04855713 .4949357 0 1
1.1884935 0 25.879080258005892 4.812184355372417 .10492945 .05894396 .6899122 0 1
. 5.755742 24.196535787431234 4.736198448394496 .14523625 .08608548 1.1286103 0 1
.6924891 6.234411 24.136589398439337 4.74493212836325 .13476273 .05329347 .6220146 0 1
.7580355 5.958425 24.152208078949545 4.7535901911063645 .1254282 .05762918 .8005841 0 1
.7836558 5.720312 24.13471053509311 4.762173934797756 .13485539 .0600852 .8276075 0 1
.7642021 6.570883 24.215400213631103 4.770684624465665 .15209243 .05421892 1.1268517 0 1
.7537746 6.526495 24.3121315669416 4.77912349311153 .1557181 .09528464 1.0803771 0 1
.7622334 6.122493 24.306479173183142 4.787491742782046 .1659288 .0975802 1.2334186 0 1
.7618092 6.113682 24.39860402051902 4.795790545596741 .16392794 .10700773 1.2668626 0 1
.8007717 5.940171 24.440441160239498 4.804021044733257 .1601782 .05361722 .9788643 0 1
.8087863 4.867535 24.472299213903636 4.812184355372417 0 .0878969 1.0270094 0 1
. 0 21.31712734644807 4.7535901911063645 .6161835 .035405505 .4728191 .0820877 0
.4618512 0 21.337435284753997 4.762173934797756 .55176055 .04723222 .56485796 .07206534 0
.5941403 0 21.36078455087655 4.770684624465665 .6694127 .05526852 .5964243 .04811 0
.6679801 0 21.4577257720642 4.77912349311153 .60712 .08645748 .5824009 .04505779 0
. 0 23.695985336242494 4.736198448394496 .7190117 .04887533 .553095 .03741924 1
.7705187 0 23.86249221018245 4.74493212836325 .7456001 .14748636 .4454397 .033855498 1
.682672 1.609438 23.65903576938081 4.7535901911063645 .3385791 .04103007 .43538275 .035442423 1
.6286231 2.484907 23.587521747769042 4.762173934797756 .3293337 .029031644 .5320368 .03727587 1
.59591955 2.564949 23.578585956841195 4.770684624465665 .3091892 .03524181 .6112404 .034720317 1
.57921785 0 23.57379891306601 4.77912349311153 .2910932 .021688854 .6228262 .05062613 1
.5702116 1.0986123 23.59059639575273 4.787491742782046 .26903787 .05534378 .7834185 .02917658 1
.5314447 1.94591 23.58866160882936 4.795790545596741 .2601817 0 1.0465562 .02840274 1
.5513356 .6931472 23.624852252665107 4.804021044733257 .2459092 .06453186 .7979235 .02741656 1
.4822459 0 24.043795315577245 4.812184355372417 .8499157 .1873104 .7865514 .02106505 1
. 0 22.28098931390445 4.770684624465665 .3840807 .09461883 .8451321 .04506727 1
.6773132 0 22.42692242480151 4.77912349311153 .3791458 .12221717 1.1784273 .04725125 1
.9319066 3.2580965 22.698318613027467 4.787491742782046 .4210063 .07018868 .8177284 .033333335 1
.8060787 2.772589 23.159944664508608 4.795790545596741 .3842118 .1145391 .7923383 .04621534 1
.8309887 1.3862944 23.28126804188242 4.804021044733257 .4161632 .10714693 .6933689 .04023709 1
.908798 .6931472 23.206337470872167 4.812184355372417 .48673666 .079771 .7126593 .04398855 1
. 0 24.037888110112725 4.663439094112067 .20535256 .07454053 .7056163 0 1
.78619 0 24.107147496432795 4.672828834461906 .20766094 .0847304 .5626268 0 1
.759084 0 23.7972083710832 4.68213122712422 .1808103 .0906814 .8042296 0 1
.6937635 0 23.81739443240191 4.6913478822291435 .18839782 .09374084 1.2695315 0 1
.7142321 0 23.8684520637118 4.700480365792417 .18411104 .09293253 1.0812703 0 1
.7059544 0 23.980323373893167 4.709530201312334 .1874382 .1009305 1.300577 0 1
.7414396 0 24.046249393101697 4.718498871295094 .19512346 .064213924 1.0034713 0 1
.6974292 0 24.196225584866482 4.727387818712341 .19427302 .08273678 1.0555807 0 1
.6635369 0 24.272739535271963 4.736198448394496 .20506677 .08730604 .7278484 0 1
.7489944 0 24.335264246751386 4.74493212836325 .23616904 .0170746 .8092557 0 1
. 6.767343 24.84321316656426 4.6443908991413725 .23399247 .1530494 1.5778688 0 1
.6145601 6.999423 24.929092141846183 4.653960350157523 .22023107 .16188905 1.1156516 0 1
.8525485 7.23201 24.901093336322354 4.663439094112067 .2153826 .12069391 1.2227247 0 1
.6608409 7.525101 24.884739312357365 4.672828834461906 .1985463 .05202068 1.0817511 0 1
.6709611 7.751045 24.99147966916149 4.68213122712422 .18963976 .09555482 .9194262 0 1
.6702911 7.624131 25.054709451257363 4.6913478822291435 .19192806 .08739167 .7840677 0 1
.656939 6.656726 25.01080320478844 4.700480365792417 .18766014 .08970646 .9115818 0 1
.8626414 6.364751 25.07917947577587 4.709530201312334 .19410613 .1058089 1.0186787 0 1
.6024605 5.986452 25.19152750567851 4.718498871295094 .19143543 .12674797 .7005265 0 1
.8392656 5.631212 25.18092160765754 4.727387818712341 .2275152 .0998321 .9155501 0 1
. 0 21.286085060398946 4.634728988229636 .18472242 -.03785776 .26592264 0 1
.799298 0 21.361346297431304 4.6443908991413725 .1597743 .05477314 .1627046 0 1
.8547568 0 21.417737140967827 4.653960350157523 .1535219 .05243392 .216123 0 1
.8669059 0 21.487178517425455 4.663439094112067 .1421967 .05316325 .2241691 0 1
.9250264 0 22.04845471091829 4.672828834461906 .10988519 .04891023 .2852529 0 1
.8776872 0 22.17889431155691 4.68213122712422 .09379284 .08198924 .5102885 0 1
.9845775 0 22.577186002682694 4.6913478822291435 .118811 .050721 .55521816 0 1
1.0301828 0 22.966175232858753 4.700480365792417 .11179797 .04159862 .3913315 0 1
.9712481 0 23.10771659050218 4.709530201312334 .10200204 .0592987 .3879843 0 1
1.0335312 0 23.14959836545931 4.718498871295094 .067471266 .03896626 .333397 0 1
. 4.875197 25.389907345032007 4.61512051684126 .19899076 .07385645 .7468041 0 1
.8884309 4.990433 25.346550952369494 4.624972813284271 .244148 .0394978 .4098155 0 1
1.0635477 5.257495 25.424400534081013 4.634728988229636 .2192063 .05449627 .640307 0 1
1.1581262 5.521461 25.41631868691919 4.6443908991413725 .2271549 .0207892 .6342526 0 1
.9200982 5.416101 25.44460887405928 4.653960350157523 .22096443 .04179115 .7652075 0 1
.9452122 4.4426513 25.540138372607856 4.663439094112067 .22103485 .032837752 .7096379 0 1
.9826649 4.4998097 25.521623333047007 4.672828834461906 .21811807 .073082656 .7592581 0 1
.94481 4.1743875 25.530462163010963 4.68213122712422 .21400774 .08259459 .8449045 0 1
.9603899 2.833213 25.546004059923572 4.6913478822291435 .21670502 .05456676 .5521333 0 1
1.0113537 2.0794415 25.51107419627515 4.700480365792417 .2304509 .029857313 .6177271 0 1
. .6931472 26.30204804427632 4.564348191467836 .23235023 .05660253 .7365565 0 1
1.0989978 0 26.461021305173926 4.574710978503383 .22211842 .0761485 .4265231 0 1
1.0847838 0 26.439131679448664 4.584967478670572 .2480531 .0520219 .5241512 0 1
1.1244308 0 26.33223200982412 4.59511985013459 .22592357 .020700116 .4890538 0 1
1.299305 0 26.368868199539683 4.605170185988092 .22564612 .015210397 .4428461 0 1
1.0879376 0 26.482558701533332 4.61512051684126 .23024334 0 .42624265 0 1
1.0736886 0 26.434295321811224 4.624972813284271 .22703527 .05220648 .53496367 0 1
1.0511376 2.0794415 26.541242693517702 4.634728988229636 .21943107 .07352107 .7395294 0 1
1.1086005 0 26.69660714579725 4.6443908991413725 .2080971 .0726368 .4884022 0 1
1.0815631 0 26.79659604452628 4.653960350157523 .18448013 .09437406 .6085445 0 1
endSaturday, May 20, 2023
Converting Unix time to human readable time format
Hi, I am having an issue converting what is assumably Unix time stamp to human readable time format.
* Example generated by -dataex-. For more info, type help dataex
When I convert them using:
the time window of the variable is in 1970s, where the actual time window is between 2019-2020.
I have searched through other threads regarding similar issues, and tried converting the ORDER_TIME variable to float,
but the results are way off from 2019-2020 time window.
So, at this point I am assuming that this might not be a Unix time stamp, or I have missed out something important converting them.
Array
Summarized ORDER_TIME has minimum value of 2, which I assume is not a valid Unix timestamp.
I'm starting to think that this might be some sort of time indicator of a day, but not sure what exactly it is indicating.
Can anyone provide advice on the matter?
Thanks in advance.
* Example generated by -dataex-. For more info, type help dataex
Code:
clear input long ORDER_TIME 205151 134137 145136 204606 211504 210942 212004 204921 205859 212712 202832 163404 212028 205544 142451 143439 155042 154539 180347 180922 180004 121746 142007 152555 162113 153127 171127 151331 161443 113412 131426 123955 123619 194452 150749 213547 131254 122751 125313 143205 171811 143902 140321 161717 140404 140210 140659 140311 213227 213112 212630 152042 153656 152006 152628 142653 140852 202313 201223 200808 144346 143442 145750 145629 151253 150323 153620 160810 154556 150338 152300 164043 191606 191427 174344 175629 180221 175809 135401 161011 160827 162033 163723 160521 94127 165558 174701 193920 102503 182007 190233 165509 185454 184617 170928 140620 180016 180752 152800 164021 end
Code:
generate double statatime = ORDER_TIME*1000 + mdyhms(1,1,1970,0,0,0) format statatime %tC list ORDER_TIME statatime, noobs
the time window of the variable is in 1970s, where the actual time window is between 2019-2020.
I have searched through other threads regarding similar issues, and tried converting the ORDER_TIME variable to float,
Code:
recast float ORDER_TIME
So, at this point I am assuming that this might not be a Unix time stamp, or I have missed out something important converting them.
Array
Summarized ORDER_TIME has minimum value of 2, which I assume is not a valid Unix timestamp.
I'm starting to think that this might be some sort of time indicator of a day, but not sure what exactly it is indicating.
Can anyone provide advice on the matter?
Thanks in advance.
Advice on backtesting when an interaction is significant
Hi all,
Currently in the works of writing my thesis where one of the regressions is the following:
Array
rhp = Real House Prices
shock = Start of unconventional monetary policy
hsr = housing supply
hhdi = household income
mr = mortgage rate
unem = unemployment
hhd = household debt
Basically I am testing whether UMP had an effect on house prices in EZ when accounting for the housing supply (which seems to be the case). However, for the period Q1 2010 - Q1 2021 (shock = Q1 2015) I want to research in which quarter the variable became significant sort of in a backtesting manner. I used the following code, but it drops all variables due to collinearity (I assume the collinearity between quarters):
gen significance = .
forval i = 1/44 {
local quarter = 200 + `i'
xtreg rhp hsr shock hsrxshock hhdi mr unem hhd if quarter == `quarter', fe
// Check the significance of the interaction term
local t_statistic_of_interaction = _b[hsrxshock]
if abs(`t_statistic_of_interaction') > 1.96 {
replace significance = `i' if missing(significance)
}
}
Anyone has any ideas on how to determine how I can test in which quarter the interaction variable became significant?
Many thanks!
Matthias
Currently in the works of writing my thesis where one of the regressions is the following:
Array
rhp = Real House Prices
shock = Start of unconventional monetary policy
hsr = housing supply
hhdi = household income
mr = mortgage rate
unem = unemployment
hhd = household debt
Basically I am testing whether UMP had an effect on house prices in EZ when accounting for the housing supply (which seems to be the case). However, for the period Q1 2010 - Q1 2021 (shock = Q1 2015) I want to research in which quarter the variable became significant sort of in a backtesting manner. I used the following code, but it drops all variables due to collinearity (I assume the collinearity between quarters):
gen significance = .
forval i = 1/44 {
local quarter = 200 + `i'
xtreg rhp hsr shock hsrxshock hhdi mr unem hhd if quarter == `quarter', fe
// Check the significance of the interaction term
local t_statistic_of_interaction = _b[hsrxshock]
if abs(`t_statistic_of_interaction') > 1.96 {
replace significance = `i' if missing(significance)
}
}
Anyone has any ideas on how to determine how I can test in which quarter the interaction variable became significant?
Many thanks!
Matthias
catplot with "empty" categories
I'd like to plot the response percentages to a question using catplot or tabplot. However some of the response categories are empty (i.e., have a value of 0), yet I cannot figure out how to include these response categories in the plot.
Here is a minimal working example:
Array
The graph is missing the 7th, 8th, 9th, and 10th decile because there is no data to plot for these categories, but I would still like to have them as part of the graph.
Alternatively, I can get close with tabplot but haven't been able to figure out how to get the value labels to display outside of each bar (as in the graph above using catplot). Here is some code and the graph it produces:
Array
The graph gets me there, other than I'd like to add values just outside of each bar (my understanding is that with tabplot I can only post them "underneath" each bar, rather than "on top" of each bar).
Would be grateful for any suggestions or tips, using either catplot or tabplot (or even hbar).
Here is a minimal working example:
Code:
use "https://www.dropbox.com/s/obmd1nqc4vvdd0l/example.dta?dl=1", clear
label define placementl 10 "Bottom 10%" 9 "2nd decile" 8 "3rd decile" 7 "4th decile" 6 "6th decile" 5 "5th decile" 4 "4th decile" 3 "3rd decile" 2 "2nd decile" 1 "Top 10%"
label val placement placementl
catplot placement, ///
title("Decision Making Ability, Relative to Others", color(black)) ///
percent ///
blabel(bar, position(outside) size(medsmall) format(%9.1f)) ///
bar(1, bcolor(orange) blcolor(none)) ///
graphregion(color(white)) ///
ylabel(0(5)30, nogrid) ///
ytitle("")The graph is missing the 7th, 8th, 9th, and 10th decile because there is no data to plot for these categories, but I would still like to have them as part of the graph.
Alternatively, I can get close with tabplot but haven't been able to figure out how to get the value labels to display outside of each bar (as in the graph above using catplot). Here is some code and the graph it produces:
Code:
use "https://www.dropbox.com/s/obmd1nqc4vvdd0l/example.dta?dl=1", clear
gen placement2 = 11 - placement
tabplot placement2, ///
horizontal ///
yasis ///
percent ///
bcolor(orange) blcolor(none) ///
graphregion(color(white)) ///
ytitle("") ///
title("Decision making ability", color(black)) ///
note("") ///
subtitle("") ///
xsize(4) ysize(6) ///
ylabel(1 "Bottom 10%" 2 "2nd decile" 3 "3rd decile" 4 "4th decile" 5 "6th decile" 6 "5th decile" 7 "4th decile" 8 "3rd decile" 9 "2nd decile" 10 "Top 10%")The graph gets me there, other than I'd like to add values just outside of each bar (my understanding is that with tabplot I can only post them "underneath" each bar, rather than "on top" of each bar).
Would be grateful for any suggestions or tips, using either catplot or tabplot (or even hbar).
coefplot line color
Hi
I was wondering how I can change the color of a graph line generated by coefplot. here is my code:
And I get the attached graph. the color of the line is blue. I want to change it to other colors. I'd appreciate your insights on that.
Thanks,Array
I was wondering how I can change the color of a graph line generated by coefplot. here is my code:
Code:
coefplot , drop(_cons) vertical baselevels ///
ci(95) ciopts(recast(rcap) alcolor(%60)) recast(connected) ///
ylabel(#9, grid) ytitle("Percentage, %") ///Thanks,Array
Friday, May 19, 2023
Graph legend(off) does not work
Hello,
I have health expenditure data for 20 countries covering 1971-2019 which looks like below. I also calculated a Fourier function to see how good the Fourier function fits the data for each country.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float year long country float id double health float fourier 1971 1 1 -.048536042892761824 -.03952992 1972 1 1 -.08843363377541565 -.04224652 1973 1 1 -.092863008288085 -.04566855 1974 1 1 -.04994182142543355 -.04974203 1975 1 1 -.0123082348645286 -.05440232 1976 1 1 -.02792827260278471 -.05957511 1977 1 1 -.025661616531818306 -.06517772 1978 1 1 -.07116244745290785 -.07112036 1979 1 1 -.09433644681980902 -.0773077 1980 1 1 -.09588154779403167 -.08364037 1981 1 1 -.07324787825170909 -.0900166 1982 1 1 -.09699708181124123 -.09633397 1983 1 1 -.09129861506032695 -.10249094 1984 1 1 -.07801576288065505 -.10838865 1985 1 1 -.08787130215726849 -.1139325 1986 1 1 -.10368869339759779 -.11903369 1987 1 1 -.12984473701132185 -.12361068 1988 1 1 -.13601273697759464 -.12759055 1989 1 1 -.13522305909266027 -.1309102 1990 1 1 -.16410929161392002 -.13351732 1991 1 1 -.1749034081219039 -.13537136 1992 1 1 -.17267929170185997 -.13644409 1993 1 1 -.15660589634579344 -.13672014 1994 1 1 -.14090811627074754 -.1361972 1995 1 1 -.1284335655668775 -.13488609 1996 1 1 -.11190290870963263 -.13281055 1997 1 1 -.11524180552098832 -.13000692 1998 1 1 -.11115354361157273 -.12652346 1999 1 1 -.1129532612256796 -.1224196 2000 1 1 -.10835246407053643 -.11776494 2001 1 1 -.11400252493988818 -.11263816 2002 1 1 -.11788591728754902 -.10712566 2003 1 1 -.11254442813587473 -.1013202 2004 1 1 -.08363546588831329 -.0953193 2005 1 1 -.09284501401780126 -.08922377 2006 1 1 -.07979523121839557 -.0831359 2007 1 1 -.06995821404301965 -.07715791 2008 1 1 -.05752852459211174 -.07139017 2009 1 1 -.07075001456909177 -.065929614 2010 1 1 -.04969200603502319 -.06086815 2011 1 1 -.03984778668039857 -.0562911 2012 1 1 -.05154969019569319 -.05227587 2013 1 1 -.0497840265353243 -.0488906 2014 1 1 -.048793576715315486 -.04619313 2015 1 1 -.04948574453129099 -.04422996 2016 1 1 -.047359177099794286 -.04303557 2017 1 1 -.044218573712996125 -.0426318 2018 1 1 -.051062423551047474 -.04302751 2019 1 1 -.06104803674991116 -.04421844 1971 2 2 -.052224026069246726 .04733511 1972 2 2 -.05553294294401772 .04135935 1973 2 2 -.03474559274618068 .03687684 1974 2 2 -.0182180229388336 .033952687 1975 2 2 .13326770223542225 .03262639 1976 2 2 .16351073041347156 .03291123 1977 2 2 .16362296723203298 .034794025 1978 2 2 .16296694361277436 .03823534 1979 2 2 .18836892476247213 .04317018 1980 2 2 .19872515475682395 .049509 1981 2 2 .02624677561653282 .05713921 1982 2 2 .013270242662924406 .065927014 1983 2 2 -.007179835216951704 .07571962 1984 2 2 -.008342505302864376 .08634771 1985 2 2 -.007111580631541289 .09762829 1986 2 2 -.008340801048958653 .1093676 1987 2 2 .0135341918959422 .1213644 1988 2 2 -.0013931907919246195 .13341318 1989 2 2 .03495434943710156 .1453076 1990 2 2 .18795793711315376 .15684384 1991 2 2 .18789604274914862 .167824 1992 2 2 .20764800747810722 .1780592 1993 2 2 .25366938276007367 .187373 1994 2 2 .2984252024669359 .19560385 1995 2 2 .2890248838655149 .20260814 1996 2 2 .27253282714672866 .20826234 1997 2 2 .2654448894076852 .2124651 1998 2 2 .26901558010899657 .21513894 1999 2 2 .26938020863676154 .2162314 2000 2 2 .24211126692168153 .2157161 2001 2 2 .20775990079827972 .21359293 2002 2 2 .18572497089675324 .20988826 2003 2 2 .1631756520898294 .20465444 2004 2 2 .1665089246092707 .1979689 2005 2 2 .14807873089046864 .18993287 2006 2 2 .1416541922510765 .18066983 2007 2 2 .14415632984664126 .1703234 2008 2 2 .14638311927339548 .1590549 2009 2 2 .13329847280662777 .14704087 2010 2 2 .13033381207252606 .1344701 2011 2 2 .10474485404573974 .1215405 2012 2 2 .11025954819939078 .10845583 2013 2 2 .10129144374916023 .09542245 2014 2 2 .0931375767153433 .08264589 2015 2 2 .07071030201701785 .070327386 2016 2 2 .0581133122787421 .05866073 2017 2 2 .05343910999252845 .04782898 2018 2 2 .04770897486693318 .03800148 2019 2 2 .04395661815196764 .0293311 1971 3 3 -.16552630479892813 -.0653828 1972 3 3 -.14708090543709273 -.05491805 end
Code:
forvalues i=1(1)20{
xtline health fourier if id==`i', name(g`i', replace) xtitle("") ylabel(,angle(0)) xlabel( 1971 1980 1990 2000 2010 2019, angle(90)) legend(off)
}
gr combine g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15 g16 g17 g18 g19 g20 ,name(Fourier_1, replace)Why do I have the legend? What am I missing? I also wonder why some graphs (i.e., Australia, Ireland and New Zealand) are smaller compared to the others. Many thanks in advance.
Array
Analysis of multiple outcome measurements with censored dependent variables
Our working group has a data set that contains about 10 measured urinary pesticides or their metabolites in a sample of about 1000 women. We want to see if we can describe exposure patterns among this sample to different socioeconomic variables, for example state of residence, age, parity, body mass index, diet, etc. Some of these explanatory variables are continuous, some ordinal and some categorical.
The dependent variables (the pesticides) are basically censored continuous variables. Many have only 10-30% of the measurements above the detection limit of the apparatus and within the group of measurements for each pesticide the distribution of measurements is highly skewed.
We are looking for help in designing a procedure that takes into account the multiple measurements of pesticides within each subject, the censored nature of the dependent variables and covariance among the various pesticides to see if we can detect patterns of pesticide exposure that might be associated with the personal characteristics of the subjects.
We’ve thought of using principal components or factor analysis but the lack of multinormality of the group of outcome measurements and the censored nature of the measurements suggests that the results of that analysis will be suspect. We are looking at “gsem” with “intreg” for solutions but aren’t sure how to specify the model.
Can any one provide pointers that would help us solve this thorny analysis problem?
The dependent variables (the pesticides) are basically censored continuous variables. Many have only 10-30% of the measurements above the detection limit of the apparatus and within the group of measurements for each pesticide the distribution of measurements is highly skewed.
We are looking for help in designing a procedure that takes into account the multiple measurements of pesticides within each subject, the censored nature of the dependent variables and covariance among the various pesticides to see if we can detect patterns of pesticide exposure that might be associated with the personal characteristics of the subjects.
We’ve thought of using principal components or factor analysis but the lack of multinormality of the group of outcome measurements and the censored nature of the measurements suggests that the results of that analysis will be suspect. We are looking at “gsem” with “intreg” for solutions but aren’t sure how to specify the model.
Can any one provide pointers that would help us solve this thorny analysis problem?
Insufficient observations with bsrifhdreg
Hello,
I was trying to conduct quantile regressions with fixed effects using the following command (for the 25th quantile):
However, I keep getting an error of insufficient observations like the following :
I have about 26000 observations, with little missing values. Is anyone familiar with this type of problem? I would be grateful if someone could help.
Regards,
I was trying to conduct quantile regressions with fixed effects using the following command (for the 25th quantile):
Code:
bsrifhdreg share process prod ln_sales capital fem_owner exported_sales foreign_own fem_leader smal_or_med, abs(country year sector) cluster(sector) rif(q(25))
Code:
insufficient observations an error occurred when bootstrap executed rifhdreg r(2001);
Regards,
Mismatch between date formatted as string and numeric date
Dear all,
I am processing hourly time series data (DD.MM.YYYY hh:mm), where the date variable is formatted as a string and looks the following:
. list datestring
datestring
1. 01.04.2018 00:00
2. 01.04.2018 01:00
3. 01.04.2018 02:00
4. 01.04.2018 03:00
5. 01.04.2018 04:00
To convert the string variable into a date variable, I run the following code:
gen Datetime = clock(datestring, "DMYhm")
format Datetime %tc
This is what I get:
. list Datetime datestring
+---------------------------------------+
| Datetime datestring |
|---------------------------------------|
1. | 01apr2018 00:00:19 01.04.2018 00:00 |
2. | 01apr2018 00:59:18 01.04.2018 01:00 |
3. | 01apr2018 02:00:28 01.04.2018 02:00 |
4. | 01apr2018 02:59:27 01.04.2018 03:00 |
5. | 01apr2018 04:00:37 01.04.2018 04:00 |
+---------------------------------------+
However, "Datetime" and "datestring" are not equivalent. For example, in row 2 the clock time is 00:59:18, whereas it should be 00:01:00.
I believe that this "mismatch" stems from the missing seconds in the variable "datestring", but I do not know how to fix this. Any help would be much appreciated!
Many thanks in advance!
Mario
I am processing hourly time series data (DD.MM.YYYY hh:mm), where the date variable is formatted as a string and looks the following:
. list datestring
datestring
1. 01.04.2018 00:00
2. 01.04.2018 01:00
3. 01.04.2018 02:00
4. 01.04.2018 03:00
5. 01.04.2018 04:00
To convert the string variable into a date variable, I run the following code:
gen Datetime = clock(datestring, "DMYhm")
format Datetime %tc
This is what I get:
. list Datetime datestring
+---------------------------------------+
| Datetime datestring |
|---------------------------------------|
1. | 01apr2018 00:00:19 01.04.2018 00:00 |
2. | 01apr2018 00:59:18 01.04.2018 01:00 |
3. | 01apr2018 02:00:28 01.04.2018 02:00 |
4. | 01apr2018 02:59:27 01.04.2018 03:00 |
5. | 01apr2018 04:00:37 01.04.2018 04:00 |
+---------------------------------------+
However, "Datetime" and "datestring" are not equivalent. For example, in row 2 the clock time is 00:59:18, whereas it should be 00:01:00.
I believe that this "mismatch" stems from the missing seconds in the variable "datestring", but I do not know how to fix this. Any help would be much appreciated!
Many thanks in advance!
Mario
How to expand the variable within group?
Here is what I am trying to do I want to make each one of the observations in the following dataset from left to right. Namely, within the group, I want each firm to have an observation with the rest of the group variables. How can I achieve this?
Array
Array
One-step stochastic frontier using Stata
Greetings, dear Statalist members!
Could I use Stata to estimate a one-stage stochastic frontier with exponential, log-normal and half-normal distributions?
I run the command below using Stata:
sfcross lny lnland lnlabour lnfertilizer lnseed, dist(hn) 0rt(o) emean (age sex education extension credit)
But I got the error message "Conditional mean model is only allowed for distribution(tnormal)".
I kindly ask you to share the commands with me.
Thank you in advance for sharing your busy schedule with me.
Could I use Stata to estimate a one-stage stochastic frontier with exponential, log-normal and half-normal distributions?
I run the command below using Stata:
sfcross lny lnland lnlabour lnfertilizer lnseed, dist(hn) 0rt(o) emean (age sex education extension credit)
But I got the error message "Conditional mean model is only allowed for distribution(tnormal)".
I kindly ask you to share the commands with me.
Thank you in advance for sharing your busy schedule with me.
Thursday, May 18, 2023
Force negative time values in 'stset'
Dear Stata users,
I wonder if there is any way to force negative time values (age-centred) in stset.
Many thanks,
Oyun
I wonder if there is any way to force negative time values (age-centred) in stset.
Code:
stset stop, enter(start) f(event=1) id(ID)
Oyun
Stata 18 data editor displaying ampersands incorrectly in string variable observations
I am experiencing an issue with the data editor displaying string ampersands (ASCII Code 38) in Stata 18.0 MP (current update level 5/15/23). Stata 17.0 MP displays them fine. Both instances running on Windows 11.
When the ampersand is between two strings, Stata 18 data editor displays an (half) underscore in the place of the ampersand and the space after. An ampersand alone displays blank, and a string of three ampersands with no spaces displays at a single ampersand. For example, this
will display in Stata 17 MP as
Array
in Stata 18 MP the data editor displays as
Array
Is there a setting that needs to be changed or is this a bug? Aside from font and alignment settings in Stata 17, all settings from query are the same between Stata 17 and 18, and Stata 18 is at the default settings. Changing the font did not change the display.
Thanks for any assistance.
When the ampersand is between two strings, Stata 18 data editor displays an (half) underscore in the place of the ampersand and the space after. An ampersand alone displays blank, and a string of three ampersands with no spaces displays at a single ampersand. For example, this
Code:
* Example generated by -dataex-. For more info, type help dataex clear input str12 var1 "Jack & Jill" "Arm & Hammer" "&" "& & & &" "&&&" " && & &" end
Array
in Stata 18 MP the data editor displays as
Array
Is there a setting that needs to be changed or is this a bug? Aside from font and alignment settings in Stata 17, all settings from query are the same between Stata 17 and 18, and Stata 18 is at the default settings. Changing the font did not change the display.
Thanks for any assistance.
Fixed effect regression questions
Hello,
i'm kinda new to stata and empirical research so sorry if this question is very basic.
1: So i have observations for different companies for different years. I have a dependent variable and several independent variables. I would like to include their industry aswell as the year in which the observations was recorded as a fixed variable.
To my understanding, one does that by going
xtset industry year
xtreg DV IV1 IV2...., fe
However i have repeated time values within this data so that doesnt work with xtset. Do i just do that by combining the industry and year into one variable and then doing the regression or do i miss something substantial?
egen industry_year = group(industry year)
xtset industry_year
xtreg DV IV1 IV2...., fe
2: As a regression result for Rsquared the overall score is used when doing xtreg, right?
3: In my mind, simply inserting the different years and industries as a dummy variable in the regression should yield the same result as in 1.
So just doing:
regress DV IV1 IV2..... year1 year2 year3.....industry1 industry2.....
should be the same as:
xtset industry_year
xtreg DV IV1 IV2...., fe
But doing so, i get a slightly different result. Why is that, or is my appraoch in 1 flawed?
Thank you in advance for your answer and sorry, if these questions are basic
i'm kinda new to stata and empirical research so sorry if this question is very basic.
1: So i have observations for different companies for different years. I have a dependent variable and several independent variables. I would like to include their industry aswell as the year in which the observations was recorded as a fixed variable.
To my understanding, one does that by going
xtset industry year
xtreg DV IV1 IV2...., fe
However i have repeated time values within this data so that doesnt work with xtset. Do i just do that by combining the industry and year into one variable and then doing the regression or do i miss something substantial?
egen industry_year = group(industry year)
xtset industry_year
xtreg DV IV1 IV2...., fe
2: As a regression result for Rsquared the overall score is used when doing xtreg, right?
3: In my mind, simply inserting the different years and industries as a dummy variable in the regression should yield the same result as in 1.
So just doing:
regress DV IV1 IV2..... year1 year2 year3.....industry1 industry2.....
should be the same as:
xtset industry_year
xtreg DV IV1 IV2...., fe
But doing so, i get a slightly different result. Why is that, or is my appraoch in 1 flawed?
Thank you in advance for your answer and sorry, if these questions are basic
ASGEN command for weighted average
Hello everyone,
I have to compute the weighted average of democratic scores in the destination countries, weighted it with my shares of emigrants in a certain province in a year.
This is the Stata command i am using:
bysort provincia year: asgen weighted_average= democracy_polity if democracy_polity!=. & share_emigration!=., weight(share_emigration)
However, the new variables doe not make much sense since the values do not sum up to 1.
Thank you,
Best,
Margherita
I have to compute the weighted average of democratic scores in the destination countries, weighted it with my shares of emigrants in a certain province in a year.
This is the Stata command i am using:
bysort provincia year: asgen weighted_average= democracy_polity if democracy_polity!=. & share_emigration!=., weight(share_emigration)
However, the new variables doe not make much sense since the values do not sum up to 1.
Thank you,
Best,
Margherita
Wednesday, May 17, 2023
[Error: icio] Missing adb tables (2001-2006)
Hello everyone, I want to ask how I can contact or report issues in using the icio command. I am currently doing a study on GVCs using the ADB tables but the periods 2001-2006 are missing, as seen from the screenshot:
Array
I understand that this usually happens when they are updating the table but I have been trying for at least a week now and it is still missing. Is this covered by Stata technical support or should I search for the icio authors?
Thank you.
Array
I understand that this usually happens when they are updating the table but I have been trying for at least a week now and it is still missing. Is this covered by Stata technical support or should I search for the icio authors?
Thank you.
two graphs
Hello
I want to combine these two graphs into one graphs please
twoway (scatter ed yearofbirth) || (lfit ed yearofbirth if yearofbirth>=1967, lstyle(dash)) || (lfit ed yearofbirth if yearofbirth < 1964, lstyle(dash)) if rural==1 , xline(1967 1965) legend(off) title(Years of School) xtitle(Birth year )
graph save ed_rural, replace
twoway (scatter ed yearofbirth) || (lfit ed yearofbirth if yearofbirth>=1967, lstyle(dash)) || (lfit ed yearofbirth if yearofbirth < 1964, lstyle(dash)) if rural==0 , xline(1967 1965) legend(off) title(Years of School) xtitle(Birth year )
graph save ed_urban, replace
data
----------------------- copy starting from the next line -----------------------
I want to combine these two graphs into one graphs please
twoway (scatter ed yearofbirth) || (lfit ed yearofbirth if yearofbirth>=1967, lstyle(dash)) || (lfit ed yearofbirth if yearofbirth < 1964, lstyle(dash)) if rural==1 , xline(1967 1965) legend(off) title(Years of School) xtitle(Birth year )
graph save ed_rural, replace
twoway (scatter ed yearofbirth) || (lfit ed yearofbirth if yearofbirth>=1967, lstyle(dash)) || (lfit ed yearofbirth if yearofbirth < 1964, lstyle(dash)) if rural==0 , xline(1967 1965) legend(off) title(Years of School) xtitle(Birth year )
graph save ed_urban, replace
data
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int yearofbirth float(rural edu) 1945 0 5.461538 1945 1 3.0535715 1946 0 4.4444447 1946 1 2.981132 1947 0 4.4583335 1947 1 3.0229886 1948 0 7.466667 1948 1 3.486111 1949 0 6.466667 1949 1 3.4285715 1950 0 5.333333 1950 1 3.837838 1951 0 7.793103 1951 1 3.505618 1952 0 7.36 1952 1 3.639535 1953 0 6.193548 1953 1 4 1954 0 6.254546 1954 1 3.862857 1955 0 6.86 1955 1 3.469388 1956 0 7.218391 1956 1 3.8533835 1957 0 6.893617 1957 1 4.0705395 1958 0 6.971428 1958 1 4.1626296 1959 0 7.011765 1959 1 3.993243 1960 0 7.4375 1960 1 3.8481014 1961 0 7.695652 1961 1 4.3138685 1962 0 7.971292 1962 1 4.3483872 1963 0 8.376344 1963 1 4.818792 1967 0 9.774648 1967 1 7.380208 1968 0 9.903571 1968 1 7.065299 1969 0 9.743494 1969 1 7.844485 1970 0 9.916924 1970 1 7.581955 1971 0 10.474684 1971 1 7.606625 1972 0 10.030556 1972 1 7.788915 1973 0 10.155216 1973 1 7.918845 1974 0 9.981396 1974 1 7.819808 1975 0 10.1309 1975 1 7.725707 1976 0 10.116773 1976 1 7.886999 1977 0 10.1833 1977 1 7.752955 1978 0 10.25701 1978 1 7.91217 1979 0 10.060362 1979 1 7.930493 1980 0 10.401316 1980 1 8.386111 1981 0 10.675324 1981 1 8.188822 1982 0 10.276224 1982 1 8.335929 1983 0 10.234568 1983 1 8.07248 1984 0 10.370723 1984 1 8.129973 1985 0 10.37156 1985 1 8.451258 1986 0 10.67 1986 1 8.335877 1987 0 10.58913 1987 1 8.426045 1988 0 10.391775 1988 1 8.287082 1989 0 10.039095 1989 1 8.25811 1990 0 10.204603 1990 1 8.161189 1991 0 10.863905 1991 1 8.934037 1992 0 10.68661 1992 1 8.7576475 1993 0 10.43131 1993 1 8.633952 1994 0 10.023728 1994 1 8.825364 1995 0 9.856209 1995 1 8.348605 1996 0 10.740332 1996 1 9.220834 1997 0 10.17801 1997 1 8.86036 end
Help to combine graph bar and connected into single graph, by group(s)
Have been trying without luck to combine the two graph types (bar and connected line) to be overlayed. Both are to be displayed by group (organization) and time period (quarter). This requires the option over() used twice- I can successfully create the graphs separately but when I try: tw (bar...) || (connected ..., c(l)), it just fails at every turn. Thanks for any help.
ERROR Fixed effect estimation (reghdfe) : treatment variable is collinear with the fixed effects
Hi all,
I am conducting a study to estimate the effect of Medicaid expansion on the uninsured rate using a classic Difference-in-Differences (DID) design with two-way fixed effects (twfe) model. My mathematical model is as follows:
UNINSist = αs + δt + βEXPANSIONist + εist
In this model:
UNINSist is a binary variable indicating whether an individual in the survey is uninsured (1) or insured (0) in state s and year t.
αs represents state fixed effects, capturing time-invariant differences across states.
δt represents time fixed effects, capturing common time trends across all states.
β is the parameter of interest, representing the causal effect of Medicaid expansion on the uninsured rate.
EXPANSIONist is a binary treatment variable that equals 1 for states that adopted Medicaid expansion and 0 for states that did not.
εist is the error term accounting for unobserved factors and random variation.
I have data from the American Community Survey (ACS) for the years 2011 to 2019, which consists of repeated cross-sectional data. Here are the top 15 observations of my dataset:
Array
To estimate this model, I am using the reghdfe command
eventhough I got the regression result I got the following error
I tired using xtreg command in Stata instead but encountered a challenge. Since my data is in a repeated cross-sectional format, the xtreg command requires me to define the panel structure using xtset ST YEAR.
To proceed with the xtreg command, I would need to aggregate the individual observations and take the mean uninsured for each state and year. This would transform my repeated cross-sectional data into a panel structure. However, I have a few concerns regarding this approach.
Firstly, my dataset includes several demographic variables such as sex, race, and education level, which are categorical variables. Aggregating the data by taking the mean may not be appropriate for categorical variables, as it could lead to the loss of valuable information. I am unsure how to handle these categorical variables effectively while converting the data to a panel structure.
Secondly, my dataset also includes survey weights. Considering that the survey weights are specific to each individual, taking the mean uninsured rate for each state and year may not accurately account for the survey design and could potentially introduce biases into the analysis.
Given these concerns, I am uncertain whether taking the average of individuals to obtain one observation per year per state is a suitable approach for my analysis. And also I don't know if taking this approach would solve my treatment collinearity with the fixed effect.
I am seeking guidance on how to address this issue and estimate the classic DID TWFE model.
Thank you for your assistance!
I'm using Stata 17
I am conducting a study to estimate the effect of Medicaid expansion on the uninsured rate using a classic Difference-in-Differences (DID) design with two-way fixed effects (twfe) model. My mathematical model is as follows:
UNINSist = αs + δt + βEXPANSIONist + εist
In this model:
UNINSist is a binary variable indicating whether an individual in the survey is uninsured (1) or insured (0) in state s and year t.
αs represents state fixed effects, capturing time-invariant differences across states.
δt represents time fixed effects, capturing common time trends across all states.
β is the parameter of interest, representing the causal effect of Medicaid expansion on the uninsured rate.
EXPANSIONist is a binary treatment variable that equals 1 for states that adopted Medicaid expansion and 0 for states that did not.
εist is the error term accounting for unobserved factors and random variation.
I have data from the American Community Survey (ACS) for the years 2011 to 2019, which consists of repeated cross-sectional data. Here are the top 15 observations of my dataset:
Array
To estimate this model, I am using the reghdfe command
Code:
reghdfe UNINS expansion , absorb(ST YEAR) cluster(ST)
Code:
note: expansion is probably collinear with the fixed effects (all partialled-out values are close to z > ero; tol = 1.0e-09) (MWFE estimator converged in 4 iterations) note: expansion omitted because of collinearity
To proceed with the xtreg command, I would need to aggregate the individual observations and take the mean uninsured for each state and year. This would transform my repeated cross-sectional data into a panel structure. However, I have a few concerns regarding this approach.
Firstly, my dataset includes several demographic variables such as sex, race, and education level, which are categorical variables. Aggregating the data by taking the mean may not be appropriate for categorical variables, as it could lead to the loss of valuable information. I am unsure how to handle these categorical variables effectively while converting the data to a panel structure.
Secondly, my dataset also includes survey weights. Considering that the survey weights are specific to each individual, taking the mean uninsured rate for each state and year may not accurately account for the survey design and could potentially introduce biases into the analysis.
Given these concerns, I am uncertain whether taking the average of individuals to obtain one observation per year per state is a suitable approach for my analysis. And also I don't know if taking this approach would solve my treatment collinearity with the fixed effect.
I am seeking guidance on how to address this issue and estimate the classic DID TWFE model.
Thank you for your assistance!
I'm using Stata 17
nested CES function
Hello,
I have a function with 4 inputs including capital(K), labor(L), energy(E), and material(M) in a nested CES form: Y=A⋅ {a[b(c⋅K^(-α)+(1-c)⋅E^(-α) )^(ρ⁄α)+(1-b)⋅L^(-ρ) ]^(β⁄ρ)+(1-a)⋅M^β }^((-1)⁄β). My dataset consists of Y, K, L, E, and M data for some manufacturing industry sub-sectors between 1975-2005. Based on this, what is the stata command for the substitution elasticity between K and E? When I use the command nlsur (Y = A * (a * (b*((cK)^(-alpha)+(1-c)((E)^(-alpha)))^(rho/alpha) + (1-b)(L)^(-rho))^(beta/rho) + (1-a)(M)^beta)^(-1/beta)), start(a = 0.5, b = 0.5, c = 0.5, alpha = 0.5, rho = 0.5, beta = 0.5, A = 1), it gives me an initial value error.
It would make me very happy if you could help.
I have a function with 4 inputs including capital(K), labor(L), energy(E), and material(M) in a nested CES form: Y=A⋅ {a[b(c⋅K^(-α)+(1-c)⋅E^(-α) )^(ρ⁄α)+(1-b)⋅L^(-ρ) ]^(β⁄ρ)+(1-a)⋅M^β }^((-1)⁄β). My dataset consists of Y, K, L, E, and M data for some manufacturing industry sub-sectors between 1975-2005. Based on this, what is the stata command for the substitution elasticity between K and E? When I use the command nlsur (Y = A * (a * (b*((cK)^(-alpha)+(1-c)((E)^(-alpha)))^(rho/alpha) + (1-b)(L)^(-rho))^(beta/rho) + (1-a)(M)^beta)^(-1/beta)), start(a = 0.5, b = 0.5, c = 0.5, alpha = 0.5, rho = 0.5, beta = 0.5, A = 1), it gives me an initial value error.
It would make me very happy if you could help.
How can I add the the decomposition of the R² in "between" and "within" to a a table (xtreg) ?
I am studying the relationship between university rankings (dependant variable) and academic freedom (independant variable). During my research, I found out that I get much more consistent results if I use cross sectional databases instead of panel databases.
I want to explain this in my report, but also want to show some proof of this. When using the
command, I get these results :
Array
I want to draw you attention on the part circled in blue. This show a decomposition of the R² in "within" (time series dimension of the data) and "between" (cross-sectional dimension of the data). Showing this in my LaTeX regression table could constitute proof that a big part of the variance of my model is explained by the cross-sectionnal aspect of my data, rather than the time series aspect.
The problem is, I don't know how I could include this in the regression table. I know how to use
and
, but I don't know if there are any options for these commands to include this particular information in the final table. Worst case scenario, I can always add it manually in LaTeX, but if there is another way to do it, I would prefer it.
I want to explain this in my report, but also want to show some proof of this. When using the
Code:
xtreg
Array
I want to draw you attention on the part circled in blue. This show a decomposition of the R² in "within" (time series dimension of the data) and "between" (cross-sectional dimension of the data). Showing this in my LaTeX regression table could constitute proof that a big part of the variance of my model is explained by the cross-sectionnal aspect of my data, rather than the time series aspect.
The problem is, I don't know how I could include this in the regression table. I know how to use
Code:
esttab
Code:
outreg
ds - how to display fullnames
Dear Statalisters -
I have a large dataset and am looking to list out the names of all my string variables. I have found the command ds which has provided the ability to limit the output of the variables to string: ds, has(type string)
Unfortunately we have some longer variable names in the dataset which means that a number of them are summarized using the ~ mid variable name. For example:
cf_antibio~r
qol_entere~y
qol_startt~e
I'd like to have the full variable name output here instead, yet the fullnames option (used within d procedure) doesn't work in this command.
Does anyone have suggestions for outputting the names of all string variables from a dataset as full names?
Thanks in advance for your advice.
Best,
Alison
I have a large dataset and am looking to list out the names of all my string variables. I have found the command ds which has provided the ability to limit the output of the variables to string: ds, has(type string)
Unfortunately we have some longer variable names in the dataset which means that a number of them are summarized using the ~ mid variable name. For example:
cf_antibio~r
qol_entere~y
qol_startt~e
I'd like to have the full variable name output here instead, yet the fullnames option (used within d procedure) doesn't work in this command.
Does anyone have suggestions for outputting the names of all string variables from a dataset as full names?
Thanks in advance for your advice.
Best,
Alison
renaming multi-variables using foreach
Hi,
I am asking this clarification after going through STATA doc and some of the web content. Although I could not get the "foreach" for "rename" completely (I am still learning that command to understand more), I felt that the examples given in the doc and web have not helped me. Hence, I am posting here my question is that I would like to run rename command for many variables in one go. For eg. the existing variables b13_q2, b13_q3, and b13_q4 have to berenamed as b13q2, b13q3, and b13q4. I do have to do this for many variables. How I was doing was in MS-excel. But I wanted to do that in STATA. I wanted to know whether "foreach" would work or is there any other command is to be called? Could someone help me outfrom this.
I am asking this clarification after going through STATA doc and some of the web content. Although I could not get the "foreach" for "rename" completely (I am still learning that command to understand more), I felt that the examples given in the doc and web have not helped me. Hence, I am posting here my question is that I would like to run rename command for many variables in one go. For eg. the existing variables b13_q2, b13_q3, and b13_q4 have to berenamed as b13q2, b13q3, and b13q4. I do have to do this for many variables. How I was doing was in MS-excel. But I wanted to do that in STATA. I wanted to know whether "foreach" would work or is there any other command is to be called? Could someone help me outfrom this.
Tuesday, May 16, 2023
command table with option markvar(newvar) creates error __000001 not found
Why do the commands table and table twoway with the option markvar(newvar)create the error "__000001 not found r(111);"? The error occurs in Stata/MP 18.0 for Windows (64-bit x86-64) and also in Stata/MP 17.0 on the same platform.
The Stata 18 help files say:
.
Yes, the table and table twoway option markvar(newvar) does what the help files say but why does it also create an error? It is possibly my user error (despite being a long-time user-programmer),
Code:
. use https://www.stata-press.com/data/r18/nhanes2l, clear
(Second National Health and Nutrition Examination Survey)
. table sex, markvar(mynewvar)
---------------------
| Frequency
---------+-----------
Sex |
Male | 4,915
Female | 5,436
Total | 10,351
---------------------
__000001 not found
r(111);
. noisily table sex diabetes, markvar(mynewvar2)
--------------------------------------------
| Diabetes status
| Not diabetic Diabetic Total
---------+----------------------------------
Sex |
Male | 4,698 217 4,915
Female | 5,152 282 5,434
Total | 9,850 499 10,349
--------------------------------------------
__000001 not found
r(111);
. tab mynewvar mynewvar2, missing
| mynewvar2
mynewvar | 0 1 | Total
-----------+----------------------+----------
1 | 2 10,349 | 10,351
-----------+----------------------+----------
Total | 2 10,349 | 10,351
markvar(newvar) generates an indicator variable that identifies the observations used in the tabulation
Yes, the table and table twoway option markvar(newvar) does what the help files say but why does it also create an error? It is possibly my user error (despite being a long-time user-programmer),
How to reshape data with dates as variable names?
Greetings everyone,
I need your help reshaping my district-level daily temperature data from a wide to a long format.
I have provided a snapshot of my data below.
In the full dataset, the dates range from 1 January 2010 to 31 December 2010.
After reshaping, my final data should have a column titled "districts", "dates" and "temperature".
I need your help reshaping my district-level daily temperature data from a wide to a long format.
I have provided a snapshot of my data below.
In the full dataset, the dates range from 1 January 2010 to 31 December 2010.
After reshaping, my final data should have a column titled "districts", "dates" and "temperature".
| 22/12/2010 | 23/12/2010 | 24/12/2010 | 25/12/2010 | 26/12/2010 | 27/12/2010 | 28/12/2010 | 29/12/2010 | 30/12/2010 | 31/12/2010 | district |
| 290.42316 | 290.901957 | 290.734693 | 290.79091 | 290.825307 | 290.452145 | 289.745414 | 290.255769 | 289.986327 | 290.213195 | Kiambu |
| 289.481211 | 289.885756 | 289.451988 | 289.767927 | 289.649287 | 289.153357 | 288.50605 | 289.048894 | 288.718642 | 288.92301 | Kirinyaga |
| 293.614437 | 293.97406 | 293.782521 | 294.27897 | 294.155162 | 293.696184 | 293.107893 | 293.79007 | 293.65899 | 293.840799 | Machakos |
| 289.207302 | 289.550169 | 289.3492 | 289.693667 | 289.658836 | 289.180405 | 288.488866 | 289.215417 | 288.847019 | 288.924391 | Murang'a |
| 289.288837 | 289.7971 | 289.618359 | 289.631393 | 290.327304 | 289.866269 | 288.860669 | 289.378584 | 289.493381 | 289.696221 | Nyandarua |
| 289.563779 | 290.08223 | 289.613974 | 289.742309 | 290.000783 | 289.236923 | 288.369696 | 289.150477 | 288.96267 | 289.007754 | Nyeri |
| 300.711855 | 300.66441 | 300.18528 | 300.410653 | 300.731155 | 300.436221 | 300.53266 | 300.752484 | 300.353821 | 300.444164 | Kilifi |
| 299.950987 | 300.133282 | 299.710488 | 299.877987 | 299.976884 | 299.445853 | 299.535585 | 300.018341 | 299.801684 | 299.864323 | Kwale |
| 301.295454 | 301.08952 | 300.471525 | 300.529891 | 301.209784 | 300.572103 | 300.901886 | 301.213524 | 300.948301 | 300.673431 | Lamu |
| 300.134964 | 300.254665 | 299.859483 | 300.240489 | 300.404015 | 299.917327 | 299.962994 | 300.233772 | 299.975998 | 300.343877 | Mombasa |
| 298.248242 | 298.587084 | 298.223512 | 298.31255 | 298.57019 | 297.938847 | 297.78606 | 298.416717 | 297.907583 | 297.759021 | Taita Taveta |
| 302.242976 | 302.18383 | 301.706147 | 301.55664 | 301.987383 | 301.566071 | 301.623938 | 301.890389 | 301.22068 | 301.330906 | Tana River |
| 292.718341 | 293.402177 | 292.95594 | 293.089097 | 293.058324 | 292.351082 | 291.739324 | 292.541505 | 292.338334 | 292.59647 | Embu |
| 300.488048 | 301.121816 | 300.67348 | 300.509714 | 300.712365 | 300.257019 | 300.060362 | 300.069667 | 299.507594 | 299.979497 | Isiolo |
| 298.730816 | 299.010331 | 298.981171 | 299.147314 | 299.156926 | 298.213189 | 297.696936 | 298.756034 | 298.212052 | 298.317001 | Kitui |