Gravity model with ppml command

[Giancarlo Carta]

Hi all,

I am a master student and I am trying to estimate a gravity model but I think I have some problems with that. I have a database with 1,663,200 observations referring to the exports and imports divided by 120 products of 22 regions in Italy with 35 countries for the period 1995-2012 . I want to include fixed effect of origin, products, time and destination.

For this I have created dummy variables for each of these using:

xi i.region*i.year, prefix(O*)
xi i.product*i.year, prefix(P*)
xi i.country*i.year, prefix(C*)

At the end of this process I have nearly 4,000 variables. Now I am trying to use ppml including first separated fixed effects for each region, product, year and country and then the combined fixed effect.

ppml dep. var. indep. variables(n°14) dummy variables for region, product, country and year, cluster(indicators of each triple link of region, country and product)

The problem is that I have started the process yesterday with the first step and Stata is still elaborating iteration (at the moment n°245). Is this normal for the dimensions of the database or there is a problem with the formulation of the command or my estimation of the model?

Thanks everybody

[Joao Santos Silva]

Dear Maria Lopez,

Most gravity models have replaced the remoteness variables with the origin and destination dummies. I suggest you do that as well.

Best wishes,

Joao

[Maria Lopez]

Dear professor Joao,

Regarding to the use of PPML for gravity model, and as I have mentioned in other discussion groups, I am doing a gravity model for Nicaragua.
I have a question. When I apply PPML and remoteness variables for the exporting and importing country, I am not sure about the sign of the remoteness variables.
For thhe exporting remoteness variable I get this is negative related with the agriculutural exports of Nicaragua.
Shoul I worry about this? or these variables are only for control. Besides of that, I got the signs expected for the variables of interest.

Thank you so much for your time.

Maria

[Joao Santos Silva]

Dear Monicucha Paganini,

I do not think the ppml command has that option. What command are you using?

Best wishes,

Joao

[Monicucha Paganini]

Dear Joao,

I wanted to ask you where the option standardize_data(0)standardize_data(0) should go in the PPML model.
thanks a lot

Monica

[Joao Santos Silva]

Dear Huong Tran,

Please show us exactly what you typed and the result you got.

Best wishes,

Joao

[Huong Tran]

Dear Joao,

I am running PPML but my dependent variable is not exports. It is Co2 emission. Does PPML work in this case? However, I got the notification: "co2 not a valid estimator". Why it happens and how I can solve it? Co2 values are nonnegative.
Many thanks,
Huong

[Joao Santos Silva]

Dear Monicucha Paganini,

I am sorry but I do not understand your question. Please rephrase it.

Best wishes,

Joao

[Monicucha Paganini]

Dear Joao,

which gravity model should I run for estimating the impact on FDI of the counterfactual full liberalization of the services sector?

thanks

[Monicucha Paganini]

Dear Joao,

I wanted to ask you which gravity model should I run to estimate the impact of on FDI based on the counterfactual of full liberalization? should I first need to obtain predicted values from a gravity model on a sector where there is full liberalization already and then apply to other sectors?


thanks

[Ainhoa Oses]

Perfect, Joao. Thanks a million for that.

Ainhoa

[Joao Santos Silva]

Not ideal, but possibly adequate because I imagine that the rate is always very far from 1.

[Ainhoa Oses]

Thanks Joao. Just a fundamental question: can PPML be used when the dependent variable is a rate? I'm using it for a gravity model where the dependent variable is the flow of migrants divided by the lagged population of origin.

Thanks again.

Ainhoa

[Joao Santos Silva]

If I understand you correctly, the country dummies are highly collinear with the country characteristics and with the lagged migration, so this is likely to result in a very unstable model. If your country variables do not vary over time they are perfectly collinear with the fixed effects and should be excluded; make sure you are not falling into that trap.

Best wishes,

Joao

[Ainhoa Oses]

My fear is that these fixed effects have really high coefficients compared to the rest of the time-varying variables of my model. This might be contributing to the explosive predictions. Would you have any suggestions on how to tackle this?

I really appreciate your help!