I am working with a gravity model and what to cluster my observations depending on if they are in the highest, middle or lowest third of observations in the respective year, but I am open for different suggestions. More on that after I explain my data and method. I use the ppmlhdfe command written by Sergio Correia, Paulo Guimarães, Thomas Zylkin. The tool can be installed with the following commands:
Code:
ssc install ftools ssc install reghdfe ssc install ppmlhdfe
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int year str3 iso3_o byte rta float(BRI_mem_o ln_exports_to_china ln_imports_from_china ln_total_china_trade) str6 country_pair float(ln_gdp_2_pop ln_preimp ln_preexpo ln_pretotal) 1990 "ABW" 0 0 . . . "ABWABW" . . . . 1991 "ABW" 0 0 . . . "ABWABW" . . . . 1992 "ABW" 0 0 . . . "ABWABW" . . . . 1993 "ABW" 0 0 . . . "ABWABW" . . . . 1994 "ABW" 0 0 . . . "ABWABW" 19.52183 . . . 1995 "ABW" 0 0 . . . "ABWABW" 19.415113 . . . 1996 "ABW" 0 0 . . . "ABWABW" 19.43265 . . . 1997 "ABW" 0 0 . . . "ABWABW" 19.588173 . . . 1998 "ABW" 0 0 . . . "ABWABW" 19.712955 . . . 1999 "ABW" 0 0 . . . "ABWABW" 19.74156 . . . 2000 "ABW" 0 0 . . . "ABWABW" 19.86799 . . . 2001 "ABW" 0 0 . . . "ABWABW" 19.87303 . . . 2002 "ABW" 0 0 . . . "ABWABW" 19.849876 . . . 2003 "ABW" 0 0 . . . "ABWABW" 19.888773 . . . 2004 "ABW" 0 0 . . . "ABWABW" 20.04846 . . . 2005 "ABW" 0 0 . . . "ABWABW" 20.11266 . . . 2006 "ABW" 0 0 . . . "ABWABW" 20.172903 . . . 2007 "ABW" 0 0 . . . "ABWABW" 20.32564 . . . 2008 "ABW" 0 0 . . . "ABWABW" 20.44747 . . . 2009 "ABW" 0 0 . . . "ABWABW" 20.224247 . . . 2010 "ABW" 0 0 . . . "ABWABW" 20.19557 . . . 2011 "ABW" 0 0 . . . "ABWABW" 20.281445 . . . 2012 "ABW" 0 0 . . . "ABWABW" . . . . 2013 "ABW" 0 0 . . . "ABWABW" . . . . 2014 "ABW" 0 0 . . . "ABWABW" . . . . 2015 "ABW" 0 0 . . . "ABWABW" . . . . 2016 "ABW" 0 0 . . . "ABWABW" . . . . 2017 "ABW" 0 0 . . . "ABWABW" 20.55063 . . . 2018 "ABW" 0 0 . . . "ABWABW" . . . . 2019 "ABW" 0 0 . . . "ABWABW" . . . . 1990 "ABW" 0 0 . . . "ABWAFG" . . . . 1991 "ABW" 0 0 . . . "ABWAFG" . . . . 1992 "ABW" 0 0 . . . "ABWAFG" . . . . 1993 "ABW" 0 0 . . . "ABWAFG" . . . . 1994 "ABW" 0 0 . . . "ABWAFG" . . . . 1995 "ABW" 0 0 . . . "ABWAFG" . . . . 1996 "ABW" 0 0 . . . "ABWAFG" . . . . 1997 "ABW" 0 0 . . . "ABWAFG" . . . . 1998 "ABW" 0 0 . . . "ABWAFG" . . . . 1999 "ABW" 0 0 . . . "ABWAFG" . . . . 2000 "ABW" 0 0 . . . "ABWAFG" . . . . 2001 "ABW" 0 0 . . . "ABWAFG" 14.68416 . . . 2002 "ABW" 0 0 . . . "ABWAFG" 15.150466 . . . 2003 "ABW" 0 0 . . . "ABWAFG" 15.234106 . . . 2004 "ABW" 0 0 . . . "ABWAFG" 15.418114 . . . 2005 "ABW" 0 0 . . . "ABWAFG" 15.587377 . . . 2006 "ABW" 0 0 . . . "ABWAFG" 15.704497 . . . 2007 "ABW" 0 0 . . . "ABWAFG" 16.085983 . . . 2008 "ABW" 0 0 . . . "ABWAFG" 16.15592 . . . 2009 "ABW" 0 0 . . . "ABWAFG" 16.222836 . . . 2010 "ABW" 0 0 . . . "ABWAFG" 16.427858 . . . 2011 "ABW" 0 0 . . . "ABWAFG" 16.560684 . . . 2012 "ABW" 0 0 . . . "ABWAFG" . . . . 2013 "ABW" 0 0 . . . "ABWAFG" . . . . 2014 "ABW" 0 0 . . . "ABWAFG" . . . . 2015 "ABW" 0 0 . . . "ABWAFG" . . . . 2016 "ABW" 0 0 . . . "ABWAFG" . . . . 2017 "ABW" 0 0 . . . "ABWAFG" 16.528923 . . . 2018 "ABW" 0 0 . . . "ABWAFG" . . . . 2019 "ABW" 0 0 . . . "ABWAFG" . . . . 1990 "ABW" 0 0 . . . "ABWAGO" . . . . 1991 "ABW" 0 0 . . . "ABWAGO" . . . . 1992 "ABW" 0 0 . . . "ABWAGO" . . . . 1993 "ABW" 0 0 . . . "ABWAGO" . . . . 1994 "ABW" 0 0 . . . "ABWAGO" 15.6064 . . . 1995 "ABW" 0 0 . . . "ABWAGO" 15.739015 . . . 1996 "ABW" 0 0 . . . "ABWAGO" 16.120626 . . . 1997 "ABW" 0 0 . . . "ABWAGO" 16.187563 . . . 1998 "ABW" 0 0 . . . "ABWAGO" 16.05207 . . . 1999 "ABW" 0 0 . . . "ABWAGO" 15.991986 . . . 2000 "ABW" 0 0 . . . "ABWAGO" 16.419592 . . . 2001 "ABW" 0 0 . . . "ABWAGO" 16.36816 . . . 2002 "ABW" 0 0 . . . "ABWAGO" 16.657751 . . . 2003 "ABW" 0 0 . . . "ABWAGO" 16.76887 . . . 2004 "ABW" 0 0 . . . "ABWAGO" 17.138466 . . . 2005 "ABW" 0 0 . . . "ABWAGO" 17.498556 . . . 2006 "ABW" 0 0 . . . "ABWAGO" 17.886463 . . . 2007 "ABW" 0 0 . . . "ABWAGO" 18.298084 . . . 2008 "ABW" 0 0 . . . "ABWAGO" 18.656734 . . . 2009 "ABW" 0 0 . . . "ABWAGO" 18.403341 . . . 2010 "ABW" 0 0 . . . "ABWAGO" 18.445055 . . . 2011 "ABW" 0 0 . . . "ABWAGO" 18.689266 . . . 2012 "ABW" 0 0 . . . "ABWAGO" . . . . 2013 "ABW" 0 0 . . . "ABWAGO" . . . . 2014 "ABW" 0 0 . . . "ABWAGO" . . . . 2015 "ABW" 0 0 . . . "ABWAGO" . . . . 2016 "ABW" 0 0 . . . "ABWAGO" . . . . 2017 "ABW" 0 0 . . . "ABWAGO" 18.593037 . . . 2018 "ABW" 0 0 . . . "ABWAGO" . . . . 2019 "ABW" 0 0 . . . "ABWAGO" . . . . 1990 "ABW" 1 0 . . . "ABWAIA" . . . . 1991 "ABW" 1 0 . . . "ABWAIA" . . . . 1992 "ABW" 1 0 . . . "ABWAIA" . . . . 1993 "ABW" 1 0 . . . "ABWAIA" . . . . 1994 "ABW" 1 0 . . . "ABWAIA" . . . . 1995 "ABW" 1 0 . . . "ABWAIA" . . . . 1996 "ABW" 1 0 . . . "ABWAIA" . . . . 1997 "ABW" 1 0 . . . "ABWAIA" . . . . 1998 "ABW" 1 0 . . . "ABWAIA" . . . . 1999 "ABW" 1 0 . . . "ABWAIA" . . . . end
I then estimate the follwing ppml regressions (one for imports, exports and total trade each) with hdfe:
Code:
ppmlhdfe imports_from_china ln_gdp_2_pop ln_preimp BRI_mem_o rta if year> 1990, absorb(year country_pair) vce(robust) ppmlhdfe exports_to_china ln_gdp_2_pop ln_preexpo BRI_mem_o rta if year> 1990, absorb(year country_pair) vce(robust) ppmlhdfe total_china_trade ln_gdp_2_pop ln_pretotal BRI_mem_o rta if year> 1990, absorb(year country_pair) vce(robust)
Code:
(dropped 3 observations that are either singletons or separated by a fixed effect)
warning: dependent variable takes very low values after standardizing (2.1798e-07)
Iteration 1: deviance = 2.1018e+10 eps = . iters = 4 tol = 1.0e-04 min(eta) = -4.02 P
Iteration 2: deviance = 5.7823e+09 eps = 2.63e+00 iters = 3 tol = 1.0e-04 min(eta) = -6.11
Iteration 3: deviance = 1.6919e+09 eps = 2.42e+00 iters = 3 tol = 1.0e-04 min(eta) = -8.86
Iteration 4: deviance = 8.5597e+08 eps = 9.77e-01 iters = 3 tol = 1.0e-04 min(eta) = -10.70
Iteration 5: deviance = 7.2615e+08 eps = 1.79e-01 iters = 3 tol = 1.0e-04 min(eta) = -11.49
Iteration 6: deviance = 6.9979e+08 eps = 3.77e-02 iters = 3 tol = 1.0e-04 min(eta) = -12.25
Iteration 7: deviance = 6.9484e+08 eps = 7.13e-03 iters = 2 tol = 1.0e-04 min(eta) = -12.74
Iteration 8: deviance = 6.9421e+08 eps = 9.11e-04 iters = 2 tol = 1.0e-04 min(eta) = -13.17
Iteration 9: deviance = 6.9417e+08 eps = 5.78e-05 iters = 2 tol = 1.0e-04 min(eta) = -13.31
Iteration 10: deviance = 6.9417e+08 eps = 9.69e-07 iters = 2 tol = 1.0e-05 min(eta) = -13.32
Iteration 11: deviance = 6.9417e+08 eps = 1.74e-09 iters = 2 tol = 1.0e-06 min(eta) = -13.32 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 11 iterations and 29 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 5,145
Absorbing 2 HDFE groups Residual df = 4,909
Wald chi2(4) = 3346.53
Deviance = 694167322.6 Prob > chi2 = 0.0000
Log pseudolikelihood = -347121413.4 Pseudo R2 = 0.9971
------------------------------------------------------------------------------
| Robust
total_chin~e | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ln_gdp_2_pop | .2087208 .0355928 5.86 0.000 .1389601 .2784815
ln_pretotal | .7336988 .0189645 38.69 0.000 .696529 .7708685
BRI_mem_o | -.0624149 .0221315 -2.82 0.005 -.1057918 -.019038
rta | -.03272 .0221649 -1.48 0.140 -.0761623 .0107223
_cons | 1.186367 .494268 2.40 0.016 .2176198 2.155115
------------------------------------------------------------------------------
Absorbed degrees of freedom:
------------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
--------------+---------------------------------------|
year | 29 0 29 |
country_pair | 204 1 203 |
------------------------------------------------------+The reason I suspect heteroskedasticity is that the estimation is not working consistent for different subsamples of countries and that the rta variable is changing signs and has very different levels of significance. But as mentioned before I am open for any other suggestion what else I could change.
Kind regards
Michael
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