My variables include 10 log price categories for alcohol types on trade and off trade : l_p_wine_on l_p_wine_off etc
I also have a log income variable : log_income
My dependent variables are the expenditure shares of the alcohol type expenditure divided by total expenditure : e.g exp_share_wine_on expshare_wine_off
I am looking at the price elasticities of demand and the cross price elasticities of demand vary across each alcohol type and vary across socio-economic groups, government regions and gender
My prices for alcohols are constant throughout the year (i am using the average year price) however they vary between years
here is a data-ex for some of my variables
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(l_p_wine_on l_p_beer_on l_p_spirits_on l_p_wine_off l_p_spirits_off > l_p_beer_off expshare_wine_on expshare_beer_off logincome) byte(socio_group > gor) int year byte sexhrp .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.433789 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .01142119 > 5.898746 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.898213 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0550356 .015000853 > 6.399842 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .0016348386 > 5.584012 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .015073973 > 7.020905 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.225338 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.911331 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.219934 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.533279 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.2492094 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .00609936 > 6.168564 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.835587 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.940566 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .006249688 > 5.331317 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.786775 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .003858888 > 7.201894 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.476967 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.009435 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .010382757 > 6.377679 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.982862 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.11283 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .0023888294 > 6.279646 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .001813489 > 6.294915 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .005435922 > 6.704463 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.747566 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .005957043 > 6.11456 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .014408222 .016718158 > 6.605068 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .018981254 0 > 6.019785 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.088818 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.779476 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .008590408 > 6.514719 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .005628793 .018012136 > 6.960443 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .005657709 > 6.424075 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.920457 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .008473212 0 > 6.898255 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .02177079 > 5.623837 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.812526 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.182973 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.514611 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.109314 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.362559 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.30903 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.26414 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.3593974 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .017695729 0 > 4.77104 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.069847 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .01336186 > 6.690271 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.80408 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.628306 5 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .016276948 > 6.522627 5 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.519619 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .035966147 > 6.422951 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.557673 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.602438 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.402017 3 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0029820926 0 > 7.401286 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .015186014 > 7.176426 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.746554 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.474176 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .01607261 > 6.874416 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .010095213 > 6.662046 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .024986824 .0423164 > 6.069906 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .001250104 0 > 7.438652 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .03693495 0 > 7.021414 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .02268917 > 6.5658 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.958667 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.192117 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0016070686 0 > 5.815264 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.34921 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.279 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.516609 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.554516 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.347932 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .01880577 0 > 5.93925 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .07133046 > 6.985651 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .005728897 .005415315 > 7.12227 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .01053234 .021376746 > 6.8088 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0019776237 0 > 6.519822 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.490757 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.787439 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.457868 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.921752 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 7.098411 2 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.400603 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .013181653 0 > 6.857086 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .003744323 0 > 6.710182 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.136498 6 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .02200635 0 > 7.438652 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .0019496685 0 > 6.911319 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .0006786454 > 6.854755 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 7.438652 2 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 6.609726 1 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 .004789272 0 > 6.868133 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.182907 4 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.823194 1 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 4.812526 6 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .04808098 > 5.530222 4 2 2007 2 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 0 > 5.793585 3 2 2007 1 .60158 -.010050327 .662688 -.9416085 -.967584 -.967584 0 .11235794 > 6.436151 1 2 2007 2 end label values gor gor label def gor 2 "north west", modify label values sexhrp sexhrp label def sexhrp 1 "male", modify label def sexhrp 2 "female", modify
Code:
tobit expshare_wine_on l_p_wine_on l_p_beer_on l_p_cider_on l_p_spirits_on l_p_alcopops_on l_p_wine_off l_p_beer_off l_p_spirits_off l_p_cider_off l_p_alcopops_off logincome i.socio_group i.gor i.year i.sexhrp , ll(0)
However my results are as follows:
Code:
. tobit expshare_wine_on l_p_wine_on l_p_beer_on l_p_cider_on l_p_spirits_on l_p_alcopops_on l_p_wine_off
> l_p_beer_off l_p_spirits_off l_p_cider_off l_p_alcopops_off logincome i.socio_group i.gor i.year i.sex
> hrp , ll(0)
note: l_p_beer_on omitted because of collinearity
note: l_p_cider_on omitted because of collinearity
note: l_p_spirits_on omitted because of collinearity
note: l_p_alcopops_on omitted because of collinearity
note: l_p_wine_off omitted because of collinearity
note: l_p_beer_off omitted because of collinearity
note: l_p_spirits_off omitted because of collinearity
note: l_p_cider_off omitted because of collinearity
note: l_p_alcopops_off omitted because of collinearity
note: 2008.year omitted because of collinearity
Refining starting values:
Grid node 0: log likelihood = -5976.9775
Fitting full model:
Iteration 0: log likelihood = -5976.9775
Iteration 1: log likelihood = -640.92644
Iteration 2: log likelihood = 1103.185
Iteration 3: log likelihood = 1808.8673
Iteration 4: log likelihood = 1909.2432
Iteration 5: log likelihood = 1910.562
Iteration 6: log likelihood = 1910.5625
Iteration 7: log likelihood = 1910.5625
Tobit regression Number of obs = 11,962
Uncensored = 2,312
Limits: lower = 0 Left-censored = 9,650
upper = +inf Right-censored = 0
LR chi2(14) = 927.97
Prob > chi2 = 0.0000
Log likelihood = 1910.5625 Pseudo R2 = -0.3207
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expshare_wine_on | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
l_p_wine_on | -.028118 .0162136 -1.73 0.083 -.0598992 .0036632
l_p_beer_on | 0 (omitted)
l_p_cider_on | 0 (omitted)
l_p_spirits_on | 0 (omitted)
l_p_alcopops_on | 0 (omitted)
l_p_wine_off | 0 (omitted)
l_p_beer_off | 0 (omitted)
l_p_spirits_off | 0 (omitted)
l_p_cider_off | 0 (omitted)
l_p_alcopops_off | 0 (omitted)
logincome | .0125922 .0006706 18.78 0.000 .0112778 .0139066
|
socio_group |
2 | .0014811 .0010997 1.35 0.178 -.0006745 .0036368
3 | -.0078991 .0012672 -6.23 0.000 -.0103829 -.0054152
4 | -.0098159 .003836 -2.56 0.011 -.0173351 -.0022968
5 | .0065436 .0035439 1.85 0.065 -.0004031 .0134903
6 | -.0027114 .0010429 -2.60 0.009 -.0047556 -.0006672
|
gor |
north west | -.0004291 .0014892 -0.29 0.773 -.0033481 .00249
merseyside | -.0009579 .0014654 -0.65 0.513 -.0038303 .0019145
yorkshire and the humber | .0017352 .0014609 1.19 0.235 -.0011284 .0045987
east midlands | -.0012567 .0021903 -0.57 0.566 -.00555 .0030366
west midlands | -.0024181 .0018331 -1.32 0.187 -.0060112 .0011751
eastern | -.0016493 .0017609 -0.94 0.349 -.005101 .0018023
|
year |
2008 | 0 (omitted)
|
sexhrp |
female | .0011694 .000803 1.46 0.145 -.0004046 .0027435
_cons | -.0853166 .0110156 -7.75 0.000 -.106909 -.0637242
--------------------------+----------------------------------------------------------------
var(e.expshare_wine_on)| .0007865 .0000268 .0007357 .0008408
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Q2. Why is the year dummy variable omitted?
I am following a model which has done close to the same thing and they didn't have this problem
Thanks so much in advance
0 Response to tobit regression with collinearity
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