Dear Economists, Statisticians & STATALIST users,
I am using panel data for 156 countries for the period (1960-2014), where the majority of variables are derived from the World Bank database except for one variable, so I have pretty much missing values. I split the sample into 5 groups to test the following model for 4 groups of countries : (MENA region, Subsaharan countries, OECD countries & Developed countries). My dependent variable is a binary variable.
Pr(Ethnic Inclusionit) = β0 + β1 OilRentsit + β2 CoalRentsit + β3 ForestRentsit+ β4 NatgasRentsit + β5 GDPPERit + β6 oilRents2it + β7 UurbanPopulationit + β8 MineralRentsit + vi+ Øt+ ɛit
The 1st Question is : I ran the Panel Probit model with random effects and the majority of variables were insignificant. However, when I ran the Probit model (Standard probit), the results had logical significant signs for the 4 groups of countries as well as for the general model, which is for the whole sample.
In fact, I know that if I have panel data, I should run the Panel Probit or Panel Logit but my question is : is there anyway to justify relying (even to some extent) on Probit model as it produces very logical results, based on literature ?
I checked the rho for the 5 models for all groups of countries and it was between ( 0 and 0.8) but the LR test of rho=0 at the end of the Panel Probit table : chibar2(01) = 2.4e+04 generates this Prob >= chibar2 = 0.0. So if the test tells me that there is a difference between Probit and Panel Probit, is there any other way to justify relying on the two techniques (Probit and Panel Probit) not only the Panel Probit ?
The 2nd Question is: Am I in need to justify in my paper why I chose the Random effects model rather than Average population ? And if Yes, what will be the explanation of choosing random effects?
Any suggestion and explanation from your part will be extremely useful to me.
Thank you so much in advance.
Sincerely,
Dana
I am using panel data for 156 countries for the period (1960-2014), where the majority of variables are derived from the World Bank database except for one variable, so I have pretty much missing values. I split the sample into 5 groups to test the following model for 4 groups of countries : (MENA region, Subsaharan countries, OECD countries & Developed countries). My dependent variable is a binary variable.
Pr(Ethnic Inclusionit) = β0 + β1 OilRentsit + β2 CoalRentsit + β3 ForestRentsit+ β4 NatgasRentsit + β5 GDPPERit + β6 oilRents2it + β7 UurbanPopulationit + β8 MineralRentsit + vi+ Øt+ ɛit
The 1st Question is : I ran the Panel Probit model with random effects and the majority of variables were insignificant. However, when I ran the Probit model (Standard probit), the results had logical significant signs for the 4 groups of countries as well as for the general model, which is for the whole sample.
In fact, I know that if I have panel data, I should run the Panel Probit or Panel Logit but my question is : is there anyway to justify relying (even to some extent) on Probit model as it produces very logical results, based on literature ?
I checked the rho for the 5 models for all groups of countries and it was between ( 0 and 0.8) but the LR test of rho=0 at the end of the Panel Probit table : chibar2(01) = 2.4e+04 generates this Prob >= chibar2 = 0.0. So if the test tells me that there is a difference between Probit and Panel Probit, is there any other way to justify relying on the two techniques (Probit and Panel Probit) not only the Panel Probit ?
The 2nd Question is: Am I in need to justify in my paper why I chose the Random effects model rather than Average population ? And if Yes, what will be the explanation of choosing random effects?
Any suggestion and explanation from your part will be extremely useful to me.
Thank you so much in advance.
Sincerely,
Dana
