Frontiers of Theoretical Econometrics

Bernd Fitzenberger develops a dynamic evaluation approach in discrete time to estimate the impact of training programs for the unemployed on employment transitions.

James H. Stock briefly reviews the large body of theory in this area, summarizes the main methods, and compares them in a range of controlled Monte Carlo experiments. The goal is to reach a recommendation for practitioners about how to compute standard errors in typical regression and GMM settings with heteroskedasticity and serial correlation, with the side constraint that it should be possible to explain the key elements of the method to an educated applied econometrician and that it should be readily implementable in common statistical software.

Alex Torgovitsky shows that sharp identied sets in a large class of econometric models can be characterized by solving linear systems of equations.

Kei Hirano considers forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use the parameter estimates for forecasting.

Bruce Hansen investigates the asymptotic integrated mean squared error (IMSE) of series regression. Least-squares and averaging least-squares estimators are investigated.

Juan Carlos Escanciano introduces nonparametric identification and estimation of consumption based asset pricing Euler equations. This entails estimation of pricing kernels or equivalently marginal utility functions up to scale.