Question
Discuss the random effects model of panel data. What are its advantages over fixed effect model ? How do we choose between random effect and fixed effect models ?
Answer :
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Skip in 10
Panel data combines cross-sectional and time series data by observing multiple individuals, firms, regions, or countries over several time periods. Such data contain both inter-individual variation and intra-individual variation over time. In econometric analysis, panel data models are widely used because they allow researchers to control for unobserved heterogeneity, improve efficiency of estimates, and study dynamics of adjustment. Among the important models for panel data analysis are the fixed effects model and the random effects model. The random effects model provides a framework in which individual-specific effects are treated as random variables rather than fixed parameters. Consider a basic linear panel data model: Y_it = β_0 + β_1 X_it + u_it where Y_it is the dependent variable for individual i at time t, X_it is a vector of explanatory variables, and u_it is the composite error term. In panel data, the error term is typically decomposed into two components: u_it = α_i + ε_it Here, α_i represents the unobserved individual-specific effect that is constant over time but varies across individuals, and ε_it is the idiosyncratic error term that varies across both individuals and time. The key distinction between fixed effects and random effects models lies in the treatment of α_i. In the random effects model, the individual-specific effect α_i is assumed to be a random variable drawn from a distribution with mean zero and constant variance. More importantly, it is assumed to be uncorrelated with the explanatory variables X_it for all time periods. That is, Cov(α_i, X_it) = 0 for all t. Under this assumption, the composite error term u_it has a particular structure that induces serial correlation within individuals over time, since α_i is common across all periods for a given individual. The random effects model can be written as: Y_it = β_0 + β_1 X_it + α_i + ε_it with α_i ~ IID(0, σ_α²) and ε_it ~ IID(0, σ_ε²), and both components independent of each other and of X_it. Because of the presence of α_i, the error term u_it exhibits
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