Dynamic Linear Models

Abstract

One of the main advantages of panel data is that it allows one to study the dynamics of economic behaviour at an individual level. Unfortunately, when dynamic models are estimated using time series of cross sections data, the usual least squares methods (such as those presented in Chapters 3 and 4) do not lead to consistent estimates for the parameters of the two most commonly used models for panel data (i.e., fixed effects and error components models). This inconsistency results from the fact that the disturbance terms are serially correlated in these models, which causes the lagged endogenous variable to be correlated with those disturbances. As in the time series context, we do not have analytical results about the small sample properties of the various estimators of these models. The only available results come from Monte-Carlo simulation studies (see Nerlove [1967], [1971]). Hence, one must rely on the asymptotic properties of these methods and assume that the size of the sample grows to infinity.