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In this video, we performed step by step the process of selecting the regression model for panel data (Random-Effects, Fixed-Effects or Pooled OLS Models), that is discussed in researches of Dougherty (2011) and Torres-Reyna (2007). Specifically, the process begins with considering whether the observations are a random sample from a given population, that is a subset of individuals randomly selected by researchers to represent an entire group as a whole. In the first step, we determine if these observations are a random sample, if this is the case, we perform the next step, otherwise we use fixed-effects model as the final decision. In case of random sample, we continue the second step by performing both fixed-effects and random-effects models, then we compare these models by using the Hausman test, also known as the Durbin-Wu-Hausman or DWH test, where the null hypothesis is that the preferred model is random effects versus the alternative the fixed effects (see Green, 2008, chapter 9). It basically tests whether the unique errors (ui) are correlated with the regressors, the null hypothesis is they are not. So, If the Hausman test indicates significant differences in the coefficients; final choice consists in Using fixed-effects model. In contrast for the third step, the Lagrange multiplier is used to decide if the random-effect model or the pool OLS model is suitable for the research. The null hypothesis in the LM test is that variances across entities is zero. This is no significant difference across units. Specifically, if LM test indicate the presence of random effects; random-effects model will be chosen; otherwise pooled OLS model will be our final decision.
7 авг 2024
