Panel data analysis combines the strengths of time series and cross-sectional data, enabling a deeper understanding of complex phenomena. Hence, it involves repeated measurements of the same variables across different…
The Random Effects Model is also sometimes called the Error Components Model or the Two-error structure approach. The Fixed Effects Model has some problems such as the use of dummy…
The Fixed Effects Model for Panel data should only be applied if the cross-sectional or time-specific effects are significant. In the case where these effects are insignificant, a simple Pooled…
The Least Squares Dummy Variables or LSDV approach is one of the ways to estimate the Fixed Effects Model. Hence, the Fixed Effects Model is sometimes called the Least Squares…
The Wu-Hausman Test can be used to determine whether the Fixed Effects Model or Random Effects Model is more appropriate. To apply this test, we need to estimate both the…
The Breusch-Pagan Lagrange Multiplier Test is used to determine whether random effects are significant in panel data models. On the other hand, the Hausman Test is used to choose between…
