Aggregation in Linear Models for Panel Data

抄録

We study the impact of individual and temporal aggregation in linear static and dynamic models for panel data in terms of i) model specification, ii) efficiency of the estimated parameters, and iii) the choice of the aggregation scheme. Model wise we find that i) individual aggregation does not affect the model structure but temporal aggregation may introduce residual autocorrelation, and ii) individual aggregation entails heteroscedasticity while temporal aggregation does not. Estimation and aggregation scheme wise we find that i) in the static model, estimation by least squares with the aggregated data entails a decrease in the efficiency of the estimated parameters and no aggregation scheme dominates in terms of efficiency, and ii) in the dynamic model, estimation with the aggregated data by GMM does not necessarily entail a decrease in the efficiency of the estimated parameters under individual aggregation, and no analytic comparison can be established for temporal aggregation, though simulations suggests that temporal aggregation deteriorates the accuracy of the estimates.