Panel data: Random effects vs. Fixed effects, and correcting serial correlation and heteroskedasticity
Hello,
For my thesis, I have a panel data set of monthly Fama French 3 factor model variables and monthly temperature (dependent: excess returns of stock; independent: SMB, HML, MF and monthly temperature). N=20, T=168 (monthly). My primary aim is to see the impact of temperature.
Hausman test and robust Hausman test (with xtoverid) tells me Random Effects is preferred over Fixed Effects model. There is serial correlation based on Woolridge's test (xtserial), and there is heteroskedasticity based on Poi & Wiggins' test for Random Individual Estimator (RE).
Q1. Is the Random Effects model suitable for the dataset?
Q2. If yes for RE, how can I account for both serial correlation and heteroskedasticity? I am confused between xtreg (with re, mle), xtgls,.., and options for autocorrelation (ar1/psar1) that work with correcting heterskedasticity.
Q3. Can I address serial correlation by lagging variables, for example: L.dependent and L.temperature?
Help would very much appreciated!
For my thesis, I have a panel data set of monthly Fama French 3 factor model variables and monthly temperature (dependent: excess returns of stock; independent: SMB, HML, MF and monthly temperature). N=20, T=168 (monthly). My primary aim is to see the impact of temperature.
Hausman test and robust Hausman test (with xtoverid) tells me Random Effects is preferred over Fixed Effects model. There is serial correlation based on Woolridge's test (xtserial), and there is heteroskedasticity based on Poi & Wiggins' test for Random Individual Estimator (RE).
Q1. Is the Random Effects model suitable for the dataset?
Q2. If yes for RE, how can I account for both serial correlation and heteroskedasticity? I am confused between xtreg (with re, mle), xtgls,.., and options for autocorrelation (ar1/psar1) that work with correcting heterskedasticity.
Q3. Can I address serial correlation by lagging variables, for example: L.dependent and L.temperature?
Help would very much appreciated!
