I am trying to find Impact of a bilateral trade agreement on agriculture trade. I am really inspired by work of Santos Silva& Tenreyro (2006) on PPML estimator and willing to use this in my research. My research contains agricultural export of Pakistan to 50 countries for 14 years so I have added two dummy variables of interest and willing to include time fixed effects.
I have following observations:
Thanking you in anticipation.
BY PPML:
predict fit, xb
(3 missing values generated)
. gen fit2=fit^2
(3 missing values generated)
. ppml agriexppk pc fta lgdpimpr lpopimpr ldistcap ler lagriland Comcol contig colony fit2 F_*
note: checking the existence of the estimates
WARNING: agriexppk has very large values, consider rescaling
WARNING: lgdpimpr has very large values, consider rescaling or recentering
WARNING: lpopimpr has very large values, consider rescaling or recentering
WARNING: lagriland has very large values, consider rescaling or recentering
WARNING: fit2 has very large values, consider rescaling or recentering
Number of regressors excluded to ensure that the estimates exist: 0
Number of observations excluded: 0
note: starting ppml estimation
note: agriexppk has noninteger values
Iteration 1: deviance = 2.23e+07
Iteration 2: deviance = 2.02e+07
Iteration 3: deviance = 2.01e+07
Iteration 4: deviance = 2.01e+07
Iteration 5: deviance = 2.01e+07
Iteration 6: deviance = 2.01e+07
Number of parameters: 25
Number of observations: 697
Pseudo log-likelihood: -10071968
R-squared: .77763997
Option strict is: off
------------------------------------------------------------------------------
| Semirobust
agriexppk | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pc | -4.023749 .7586098 -5.30 0.000 -5.510597 -2.536901
fta | -2.207587 .4853811 -4.55 0.000 -3.158916 -1.256257
lgdpimpr | -.4123445 .0944844 -4.36 0.000 -.5975306 -.2271584
lpopimpr | 1.148529 .2470537 4.65 0.000 .6643125 1.632745
ldistcap | 3.48049 .6613658 5.26 0.000 2.184237 4.776743
ler | -.2522311 .0602458 -4.19 0.000 -.3703108 -.1341514
lagriland | -.6025691 .1250249 -4.82 0.000 -.8476133 -.3575249
Comcol | -1.056128 .2360891 -4.47 0.000 -1.518854 -.5934016
contig | 1.961385 .3515048 5.58 0.000 1.272448 2.650321
colony | -2.435843 .5272419 -4.62 0.000 -3.469218 -1.402468
fit2 | .1519963 .0219224 6.93 0.000 .1090292 .1949633
F_1Year_2002 | -.1669563 .1991266 -0.84 0.402 -.5572373 .2233247
F_1Year_2003 | -.6412399 .2368302 -2.71 0.007 -1.105419 -.1770611
F_1Year_2004 | -.677247 .2397869 -2.82 0.005 -1.147221 -.2072734
F_1Year_2005 | -1.242436 .3274378 -3.79 0.000 -1.884203 -.60067
F_1Year_2006 | -1.243469 .3439022 -3.62 0.000 -1.917505 -.5694329
F_1Year_2007 | -1.479224 .3537169 -4.18 0.000 -2.172496 -.7859518
F_1Year_2008 | -2.61203 .5906815 -4.42 0.000 -3.769745 -1.454316
F_1Year_2009 | -2.129372 .4768287 -4.47 0.000 -3.063939 -1.194805
F_1Year_2010 | -2.591113 .552161 -4.69 0.000 -3.673329 -1.508897
F_1Year_2011 | -3.527925 .7377195 -4.78 0.000 -4.973829 -2.082021
F_1Year_2012 | -3.285037 .6943818 -4.73 0.000 -4.646 -1.924074
F_1Year_2013 | -3.496063 .7346149 -4.76 0.000 -4.935881 -2.056244
F_1Year_2014 | -3.385662 .7185074 -4.71 0.000 -4.793911 -1.977414
_cons | -34.60186 7.886442 -4.39 0.000 -50.059 -19.14472
------------------------------------------------------------------------------
. test fit2=0
( 1) fit2 = 0
chi2( 1) = 48.07
Prob > chi2 = 0.0000
BY OLS:
predict fit, xb
(3 missing values generated)
. gen fit2=fit^2
(3 missing values generated)
. regres lagriexppk pc fta lgdpimpr lpopimpr ldistcap ler lagriland Comcol contig colony fit2 F_*, robust
Linear regression Number of obs = 697
F( 24, 672) = 33.25
Prob > F = 0.0000
R-squared = 0.4673
Root MSE = 1.3866
------------------------------------------------------------------------------
| Robust
lagriexppk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pc | 9.969214 2.239035 4.45 0.000 5.572868 14.36556
fta | 4.915393 1.075384 4.57 0.000 2.803876 7.026909
lgdpimpr | 1.288349 .3207756 4.02 0.000 .658506 1.918192
lpopimpr | -.7609452 .1952006 -3.90 0.000 -1.144222 -.3776688
ldistcap | -10.49277 2.335135 -4.49 0.000 -15.07781 -5.907733
ler | .438851 .1066996 4.11 0.000 .2293463 .6483556
lagriland | .9132004 .2022078 4.52 0.000 .5161653 1.310236
Comcol | 5.458224 1.22512 4.46 0.000 3.0527 7.863749
contig | -7.6576 1.751437 -4.37 0.000 -11.09655 -4.218653
colony | 6.331565 1.34494 4.71 0.000 3.690775 8.972355
fit2 | -.1536268 .0397374 -3.87 0.000 -.2316511 -.0756025
F_1Year_2002 | 3.049975 .9281477 3.29 0.001 1.227557 4.872394
F_1Year_2003 | 4.174452 1.14771 3.64 0.000 1.920923 6.427982
F_1Year_2004 | 4.756569 1.262242 3.77 0.000 2.278156 7.234981
F_1Year_2005 | 6.016777 1.519713 3.96 0.000 3.032819 9.000735
F_1Year_2006 | 6.08904 1.534441 3.97 0.000 3.076164 9.101916
F_1Year_2007 | 6.755335 1.665978 4.05 0.000 3.484186 10.02648
F_1Year_2008 | 8.696563 2.076598 4.19 0.000 4.619163 12.77396
F_1Year_2009 | 8.503707 2.044425 4.16 0.000 4.489477 12.51794
F_1Year_2010 | 9.50537 2.256293 4.21 0.000 5.075138 13.9356
F_1Year_2011 | 11.17181 2.611969 4.28 0.000 6.043205 16.30041
F_1Year_2012 | 10.79001 2.529944 4.26 0.000 5.822469 15.75756
F_1Year_2013 | 11.88694 2.770768 4.29 0.000 6.446531 17.32734
F_1Year_2014 | 11.30623 2.645622 4.27 0.000 6.11155 16.50091
_cons | 107.5101 21.09863 5.10 0.000 66.08291 148.9373
------------------------------------------------------------------------------
. test fit2=0
( 1) fit2 = 0
F( 1, 672) = 14.95
Prob > F = 0.0001
I have following observations:
- My RESET results are not looking consistent, please guide me is there any possible reason for this and any suggestions to correct it.
- My data do not contain zero value of trade, in this case, do I still need to prefer PPML over OLS?
Thanking you in anticipation.
BY PPML:
predict fit, xb
(3 missing values generated)
. gen fit2=fit^2
(3 missing values generated)
. ppml agriexppk pc fta lgdpimpr lpopimpr ldistcap ler lagriland Comcol contig colony fit2 F_*
note: checking the existence of the estimates
WARNING: agriexppk has very large values, consider rescaling
WARNING: lgdpimpr has very large values, consider rescaling or recentering
WARNING: lpopimpr has very large values, consider rescaling or recentering
WARNING: lagriland has very large values, consider rescaling or recentering
WARNING: fit2 has very large values, consider rescaling or recentering
Number of regressors excluded to ensure that the estimates exist: 0
Number of observations excluded: 0
note: starting ppml estimation
note: agriexppk has noninteger values
Iteration 1: deviance = 2.23e+07
Iteration 2: deviance = 2.02e+07
Iteration 3: deviance = 2.01e+07
Iteration 4: deviance = 2.01e+07
Iteration 5: deviance = 2.01e+07
Iteration 6: deviance = 2.01e+07
Number of parameters: 25
Number of observations: 697
Pseudo log-likelihood: -10071968
R-squared: .77763997
Option strict is: off
------------------------------------------------------------------------------
| Semirobust
agriexppk | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pc | -4.023749 .7586098 -5.30 0.000 -5.510597 -2.536901
fta | -2.207587 .4853811 -4.55 0.000 -3.158916 -1.256257
lgdpimpr | -.4123445 .0944844 -4.36 0.000 -.5975306 -.2271584
lpopimpr | 1.148529 .2470537 4.65 0.000 .6643125 1.632745
ldistcap | 3.48049 .6613658 5.26 0.000 2.184237 4.776743
ler | -.2522311 .0602458 -4.19 0.000 -.3703108 -.1341514
lagriland | -.6025691 .1250249 -4.82 0.000 -.8476133 -.3575249
Comcol | -1.056128 .2360891 -4.47 0.000 -1.518854 -.5934016
contig | 1.961385 .3515048 5.58 0.000 1.272448 2.650321
colony | -2.435843 .5272419 -4.62 0.000 -3.469218 -1.402468
fit2 | .1519963 .0219224 6.93 0.000 .1090292 .1949633
F_1Year_2002 | -.1669563 .1991266 -0.84 0.402 -.5572373 .2233247
F_1Year_2003 | -.6412399 .2368302 -2.71 0.007 -1.105419 -.1770611
F_1Year_2004 | -.677247 .2397869 -2.82 0.005 -1.147221 -.2072734
F_1Year_2005 | -1.242436 .3274378 -3.79 0.000 -1.884203 -.60067
F_1Year_2006 | -1.243469 .3439022 -3.62 0.000 -1.917505 -.5694329
F_1Year_2007 | -1.479224 .3537169 -4.18 0.000 -2.172496 -.7859518
F_1Year_2008 | -2.61203 .5906815 -4.42 0.000 -3.769745 -1.454316
F_1Year_2009 | -2.129372 .4768287 -4.47 0.000 -3.063939 -1.194805
F_1Year_2010 | -2.591113 .552161 -4.69 0.000 -3.673329 -1.508897
F_1Year_2011 | -3.527925 .7377195 -4.78 0.000 -4.973829 -2.082021
F_1Year_2012 | -3.285037 .6943818 -4.73 0.000 -4.646 -1.924074
F_1Year_2013 | -3.496063 .7346149 -4.76 0.000 -4.935881 -2.056244
F_1Year_2014 | -3.385662 .7185074 -4.71 0.000 -4.793911 -1.977414
_cons | -34.60186 7.886442 -4.39 0.000 -50.059 -19.14472
------------------------------------------------------------------------------
. test fit2=0
( 1) fit2 = 0
chi2( 1) = 48.07
Prob > chi2 = 0.0000
BY OLS:
predict fit, xb
(3 missing values generated)
. gen fit2=fit^2
(3 missing values generated)
. regres lagriexppk pc fta lgdpimpr lpopimpr ldistcap ler lagriland Comcol contig colony fit2 F_*, robust
Linear regression Number of obs = 697
F( 24, 672) = 33.25
Prob > F = 0.0000
R-squared = 0.4673
Root MSE = 1.3866
------------------------------------------------------------------------------
| Robust
lagriexppk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pc | 9.969214 2.239035 4.45 0.000 5.572868 14.36556
fta | 4.915393 1.075384 4.57 0.000 2.803876 7.026909
lgdpimpr | 1.288349 .3207756 4.02 0.000 .658506 1.918192
lpopimpr | -.7609452 .1952006 -3.90 0.000 -1.144222 -.3776688
ldistcap | -10.49277 2.335135 -4.49 0.000 -15.07781 -5.907733
ler | .438851 .1066996 4.11 0.000 .2293463 .6483556
lagriland | .9132004 .2022078 4.52 0.000 .5161653 1.310236
Comcol | 5.458224 1.22512 4.46 0.000 3.0527 7.863749
contig | -7.6576 1.751437 -4.37 0.000 -11.09655 -4.218653
colony | 6.331565 1.34494 4.71 0.000 3.690775 8.972355
fit2 | -.1536268 .0397374 -3.87 0.000 -.2316511 -.0756025
F_1Year_2002 | 3.049975 .9281477 3.29 0.001 1.227557 4.872394
F_1Year_2003 | 4.174452 1.14771 3.64 0.000 1.920923 6.427982
F_1Year_2004 | 4.756569 1.262242 3.77 0.000 2.278156 7.234981
F_1Year_2005 | 6.016777 1.519713 3.96 0.000 3.032819 9.000735
F_1Year_2006 | 6.08904 1.534441 3.97 0.000 3.076164 9.101916
F_1Year_2007 | 6.755335 1.665978 4.05 0.000 3.484186 10.02648
F_1Year_2008 | 8.696563 2.076598 4.19 0.000 4.619163 12.77396
F_1Year_2009 | 8.503707 2.044425 4.16 0.000 4.489477 12.51794
F_1Year_2010 | 9.50537 2.256293 4.21 0.000 5.075138 13.9356
F_1Year_2011 | 11.17181 2.611969 4.28 0.000 6.043205 16.30041
F_1Year_2012 | 10.79001 2.529944 4.26 0.000 5.822469 15.75756
F_1Year_2013 | 11.88694 2.770768 4.29 0.000 6.446531 17.32734
F_1Year_2014 | 11.30623 2.645622 4.27 0.000 6.11155 16.50091
_cons | 107.5101 21.09863 5.10 0.000 66.08291 148.9373
------------------------------------------------------------------------------
. test fit2=0
( 1) fit2 = 0
F( 1, 672) = 14.95
Prob > F = 0.0001
Comment