[Milad Aminizadeh]

Dear Joao,

I have an important question

What is your suggestion for my research? PPML or OLS?????????

[Milad Aminizadeh]

Dear Joao

yes, I can show.


. predict fit, xb

. gen fit2=fit^2

. ppml value lgdpx lgdpi lgdppx lgdppi ldis landli landlx fit2, cluster( ldis)


. test fit2=0

( 1) fit2 = 0

chi2( 1) = 9.31
Prob > chi2 = 0.0023


Milad

[Joao Santos Silva]

Thanks. You have more zeros than what I expected so OLS clearly is not a good choice. Can you show us the code used for the RESET in PPML?

Cheers,

Joao

[Milad Aminizadeh]

dear Joao

yes I can.

reg lvalue lgdpx lgdpi lgdppx lgdppi ldis landli landlx, cluster( ldis)

Linear regression Number of obs = 174
F( 7, 126) = 25.61
Prob > F = 0.0000
R-squared = 0.3298
Root MSE = 1.988

(Std. Err. adjusted for 127 clusters in ldis)
------------------------------------------------------------------------------
| Robust
lvalue | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lgdpx | -.2589429 .1604681 -1.61 0.109 -.5765046 .0586188
lgdpi | .8058141 .1025243 7.86 0.000 .6029215 1.008707
lgdppx | .0675892 .2106754 0.32 0.749 -.3493312 .4845096
lgdppi | -.0855848 .2723315 -0.31 0.754 -.6245209 .4533512
ldis | -.966287 .1909489 -5.06 0.000 -1.344169 -.5884047
landli | .4627078 .472598 0.98 0.329 -.4725497 1.397965
landlx | 2.135198 .2848611 7.50 0.000 1.571466 2.69893
_cons| 6.874997 1.926631 3.57 0.001 3.062251 10.68774
------------------------------------------------------------------------------


RESET test:


. predict fit, xb

. gen fit2=fit^2

. reg lvalue lgdpx lgdpi lgdppx lgdppi ldis landli landlx fit2, cluster( ldis)

. test fit2=0

( 1) fit2 = 0

F( 1, 126) = 1.14
Prob > F = 0.2873

[Joao Santos Silva]

This is a very atypical dataset because it surely does not have the zeros and the heteroskedasticity that characterize trade data and motivate the use of PPML. This, however, may explain why PPML has no advantage over OLS, but does not explain the superiority of OLS. Can you please show us the commands you used to perform the RESET tests and the OLS results?

Joao

[Milad Aminizadeh]

Dear Joao,

Thank you for your reply.

My data is:

Dependent Variable: Export of Dates to EU countries in 2013
Exporters: 12 countries (Top Exporters such as Tunisia, Saudi Arabia, …)
Importers: 28 countries (European Union)
Year: 2013


ppml value lgdpx lgdpi lgdppx lgdppi ldis landli landlx, cluster(ldis)

note: checking the existence of the estimates

Number of regressors excluded to ensure that the estimates exist: 0
Number of observations excluded: 0

note: starting ppml estimation

Iteration 1: deviance = 430207
Iteration 2: deviance = 329435.5
Iteration 3: deviance = 314276.6
Iteration 4: deviance = 312864.6
Iteration 5: deviance = 312608.8
Iteration 6: deviance = 312569.2
Iteration 7: deviance = 312567.4
Iteration 8: deviance = 312567.4
Iteration 9: deviance = 312567.4


Number of parameters: 8
Number of observations: 334
Pseudo log-likelihood: -156873.22
R-squared: .5871964
Option strict is: off
(Std. Err. adjusted for 172 clusters in ldis)
--------------------------------------------------------------------------------------------------------------
| Robust
value | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------------------------------------------------------------------------
lgdpx | -.8473654 .1630437 -5.20 0.000 -1.166925 -.5278056
lgdpi | 1.025005 .1124659 9.11 0.000 .8045761 1.245434
lgdppx | .3310993 .1892837 1.75 0.080 -.0398899 .7020884
lgdppi | .2646335 .2307802 1.15 0.252 -.1876874 .7169544
ldis | -.6218436 .1669692 -3.72 0.000 -.9490972 -.29459
landli | .2435175 .3134024 0.78 0.437 -.3707399 .8577748
landlx | 4.922072 .6066905 8.11 0.000 3.73298 6.111163
_cons | 2.654003 1.778816 1.49 0.136 -.8324121 6.140418
--------------------------------------------------------------------------------------------------------------

[Joao Santos Silva]

Dear Milad,

Without knowing what are the models you are estimating and the kind of data you have it is impossible to comment on this result. Maybe you should start a new thread for your question.

Best wishes,

Joao

[Milad Aminizadeh]

Hi all,

I have a question. I estimate gravity model by PPML and OLS estimators. RESET test p-value in OLS is equal 0.287 and in PPML is equal 0.002.

my result is different from Silva & Tenreyro's study.why????????

OLS Estimation:

test fit2=0

( 1) fit2 = 0

F( 1, 126) = 1.14
Prob > F = 0.2873


PPML Estimation:

test fit2=0

( 1) fit2 = 0

chi2( 1) = 9.31
Prob > chi2 = 0.0023



Do you think my result is wrong??????????????

[Nick Cox]

Missing reference here:

SJ-11-2 st0225 . . . . . . . . . . . . . . . poisson: Some convergence issues
(help ppml if installed) . . . J. M. C. Santos Silva and S. Tenreyro
Q2/11 SJ 11(2):207--212
provides improved Poisson regression by checking for the
existence of the estimates and providing two methods for
dropping regressors that cause nonexistence of estimates

[Joao Santos Silva]

No, not a problem at all; glad it worked!

Joao

[Giancarlo Carta]

Thank you Joao

it does work now. Anyway Stata tells me my dep variable have non integer values (obviously after rescaling by 1e3). Is this a problem?

Thank you

[Giancarlo Carta]

Really thank you, I will let you know as I try again.

Giancarlo

[Joao Santos Silva]

Dear Giancarlo,

Rescaling the variables should help (notice that the need to rescale is specific to Stata, with most other softwares rescaling is not needed). With such large model, estimation will always take some time.

Good luck with your work,

Joao

[Giancarlo Carta]

Dear Joao,

thank you for your reply. I will try to rescale dep variable (and independent ones I suppose too?) and see what happens. I tried different types of regression in order to estimate best the model. It was a suggestion of my professor the use of ppml and fixed effect in this way.

Anyway I'm pretty new to Stata so I have no idea how long it takes such a process. If you have any suggestion of a more suitable command/process, that is more than welcome.

Thank you

[Joao Santos Silva]

Dear Giancarlo,

The first warning that you get is that your dependent variable has very large values. If you rescale it (say, divide it by 1e3 or 1e6), the problem may go away. On a side note, you need to think about whether you are asking too much from your data.

All the best,

Joao