Hi,
I am working with STATA/SE 17.0. I have a panel data for 10 countries for the 2000-2021 period.
My dependent variable is a count variable, and the variance is much bigger than the mean.
I decided to use the negative binomial model.
I have a few questions:
1) Do I need to check for stationarity before doing the regression? If not can you tell me any literature where I can read about stationarity on count data?
2) What do you advise to do to check if the results are robust?
The code I have is the follow.
// Import your data
import excel using "TOP10_efficiency.xlsx", firstrow clear
save "Data_efficiency.dta", replace // save data
// Declare it as panel data
xtset ID year, yearly // declare it to be panel data
duplicates report ID year
// Descriptive Statistics
summarize
// Dependent variable details
sum patentsEFF, detail
// Correlation matrix
correlate patentsEFF patentsRE EPSindex oilprice RE_RD fossilfuelsrent gdpcapita PARIS
// Creating the multiplicative dummy for the years 2015-2021
generate year_dummy = (year >= 2015 & year <= 2021)
generate gdp_per_capita_x_year_dummy = gdpcapita * year_dummy
// Principal regression
xtnbreg L.patentsEFF L.patentsRE EPSindex oilprice RE_RD fossilfuelsrent gdp_per_capita_x_year_dummy gdpcapita PARIS, fe noconstant
Thank you
I am working with STATA/SE 17.0. I have a panel data for 10 countries for the 2000-2021 period.
My dependent variable is a count variable, and the variance is much bigger than the mean.
I decided to use the negative binomial model.
I have a few questions:
1) Do I need to check for stationarity before doing the regression? If not can you tell me any literature where I can read about stationarity on count data?
2) What do you advise to do to check if the results are robust?
The code I have is the follow.
// Import your data
import excel using "TOP10_efficiency.xlsx", firstrow clear
save "Data_efficiency.dta", replace // save data
// Declare it as panel data
xtset ID year, yearly // declare it to be panel data
duplicates report ID year
// Descriptive Statistics
summarize
// Dependent variable details
sum patentsEFF, detail
// Correlation matrix
correlate patentsEFF patentsRE EPSindex oilprice RE_RD fossilfuelsrent gdpcapita PARIS
// Creating the multiplicative dummy for the years 2015-2021
generate year_dummy = (year >= 2015 & year <= 2021)
generate gdp_per_capita_x_year_dummy = gdpcapita * year_dummy
// Principal regression
xtnbreg L.patentsEFF L.patentsRE EPSindex oilprice RE_RD fossilfuelsrent gdp_per_capita_x_year_dummy gdpcapita PARIS, fe noconstant
Thank you
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