Large N, T asymptotic analysis of panel data models with incidental parameters

This dissertation contributes to the econometrics of panel data models and their application to economic problems. In particular, it considers "large T" panels, where in addition to the cross-sectional dimension N also the number of time periods T is relatively large. Chapter 1 provides an introduction to the field of large T panel data econometrics and explains the contribution of the dissertation to this field.;Chapter 2 analyzes linear panel regression models, allowing for unobserved factors (interactive fixed effects) in the error structure of the model. In particular, it is shown that, under appropriate assumptions, the limiting distribution of the Gaussian quasi maximum likelihood estimator for the regression coefficients is independent of the number of factors used in the estimation. The important practical implication of this result is that for inference on the regression coefficients there is no need to estimate the number of factors consistently.;Chapter 3 extends the Berry, Levinsohn and Pakes (1995) random coefficients discretechoice demand model by adding interactive fixed effects to the unobserved product characteristics of this model. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics, which accommodates endogeneity, and they can capture strong persistence in market shares across products and markets. A two step least squares-minimum distance procedure is proposed to estimate the model, and the asymptotic properties of this estimator are derived. This methodology is then applied to the estimation of US automobile demand.;Chapter 4 proposes a new approach for higher order bias correction in large T non-linear panel data models that is based on inference on the individual effect distribution. Under appropriate assumptions it is shown that the incidental parameter bias for the estimator of the parameters of interest can converge to zero at an arbitrary polynomial rate in T, i.e. that the incidental parameter problem can vanish very rapidly in this approach as T increases. This has important implications in particular for applications where T is modestly large and N is much larger than T.