9807111 Hansen This project develops new tools for econometric estimation and inference in two distinct areas of interest. (1) New bootstrap confidence intervals for cases where t-statistics are not approximately free of nuisance parameters. As a leading example of interest, the distribution of the t-statistic in the AR(1) model with a local-to-unity autoregressive root is not asymptotically pivotal. Similar difficulties arise in many dynamic and nonlinear models. Standard bootstrap methods do not work properly in such contexts. The project calculates bootstrap distributions on a grid of the parameter of interest, holding all nuisance parameters fixed at the sample estimates. Accurate bootstrap confidence intervals are easy to construct from such bootstrap distributions and Monte Carlo simulations show important gains in confidence interval coverage. These techniques are extended to handle confidence region construction for vector-valued parameters. Extensions to non-linear econometric models will also be considered. (2) Threshold models. A threshold arises when a sample is split into groups based on a continuously-distributed covariate. The difficulty in inference concerns estimation of the unknown threshold. It is known that the least-squares estimate is super-consistent and has a non-standard asymptotic distribution. A new asymptotic approximation to the least squares criterion function is developed and used to construct asymptotically valid confidence intervals. The methods are extended to non-dynamic panels, and will be extended to 2SLS estimation. Threshold autoregressive models are especially popular in the time series literature. ??
