Chapter 22 Panel data

Publisher Summary

This chapter discusses the models that are static conditional on a latent variable. The panel aspect of the data has been primarily used to control for the latent variable. Much work needs to be done on models that incorporate uncertainty and interesting dynamics. Exploiting the martingale implications of time additive utility seems fruitful. There is, however, a potentially important distinction between time averages and cross-section averages. A time average of forecast errors over T periods should converge to zero as T→ ∞. But an average of forecast errors across N individuals surely need not converge to zero as N→ ∞; there is a common component in those errors, due to economy-wide innovations. The same point applies when considering covariances of forecast errors with variables that are in the agent's information sets. If those conditioning variables are discrete, one can think of averaging over subsets of the forecast errors; as T→ ∞, these averages should converge to zero but not necessarily as N → ∞.