I am using a panel data set, where, my dependent variable is a score, moving slowly over time (24 years). Is it appropriate to apply a fixed effect model?. I have come across slides on "Panel data analysis using fixed and random effect using STATA" by Princeton University ( https://www.princeton.edu/~otorres/Panel101.pdf ). On slide 10th, they have mentioned that " Fixed-effects will not work well with data for which within-cluster variation is minimal or for slow-changing variables over time". Is it applicable to a slow-changing dependent variable?
This is indeed an interesting issue. Low within-group variation can amplify measurement error.
I suggest you to try several things:
(1) Use long differences (e.g., you have 24 years, try pooling 12-year lapses). You should get more variation in this way. You can also formally compute the wg variance and bw variance to get an idea of what is happening
(2) Explore random effects controlling for a wide set of covariates.
(3) Explore mixed approaches (e.g., correlated random effects)
José-Ignacio Antón Thanks for the suggestions. I got 0.40 within R -square with the fixed-effect model and every less between R -square (0.11). There are four issues in my data (heteroscedasticity, auto-correlation, cross-sectional dependency, endogeneity). The most appropriate method to deal with all of these simultaneously is the two-step system GMM. However, my dependent variable is an index that is moving very slowly over time. It means when we difference the variable (X2-X1) (0.51234-0.51223), the resultant values are very less. I tried to apply the two-step system GMM to my model but I got unsatisfactory results (the coefficients of all the variables become insignificant).
I applied PCSE with regional dummies to solve this issue and took lags of the endogenous variable. Am I on the right path?
Sorry for adding more to the problem rather the solution, but showily changing values in variable may be a sign of the presence of non-stationarity. Have you performed panel data unit root tests in your variables?
Pandelis Mitsis Yes, I have applied the unit-root test and all the variables are stationary at first difference.
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