Hello professor Joao Santos Silva,👋
I’m working on a gravity model to estimate the effects of SPS (Sanitary and Phytosanitary) measures imposed by France, Spain, and the UK on Moroccan agricultural exports (fruits, vegetables, etc.). My dataset is multidimensional, including 15 different product categories over time, and I’m using yearly data for Morocco’s top 3 trading partners.
I’ve heard that PPML is the most appropriate method for this case. However, I’m struggling with the correct way to prepare my data in order to estimate.
Here are the specifics of my setup:
Thanks in advance for any insights you can share!
Best regards
Yassine Touzani.
I’m working on a gravity model to estimate the effects of SPS (Sanitary and Phytosanitary) measures imposed by France, Spain, and the UK on Moroccan agricultural exports (fruits, vegetables, etc.). My dataset is multidimensional, including 15 different product categories over time, and I’m using yearly data for Morocco’s top 3 trading partners.
I’ve heard that PPML is the most appropriate method for this case. However, I’m struggling with the correct way to prepare my data in order to estimate.
Here are the specifics of my setup:
- Country level: Morocco vs. France, Spain, UK
- Product level: 15 agricultural product categories (HS codes)
- Time dimension: Yearly data
- My model contains; a dummy variable for the existence of SPS notifications for a specific product in each year, and also a variable that catch the intensity of each notification, in additional to other control variables...
- How should I implement fixed effects in my model?
But I’m also considering product fixed effects due to the multidimensional nature of the data. - I understand that for PPML, the dependent variable should not be logged. Does this apply to all explanatory variables as well, like distance, GDP, and population?
- Any tips on handling the multidimensional panel structure (countries, products, and time) efficiently in Stata knowing that i'm new in Stata :/ ?
Thanks in advance for any insights you can share!
Best regards
Yassine Touzani.

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