题 目：Large Dimensional Discrete Panel Data Modeling: Theory and Applications

报告人：金赛男副教授（成人直播商务统计与经济计量系）

时 间：2007年9月26日（周三）10：00-11：00

地 点：成人直播202室

Abstract

This paper develops a regression limit theory for discrete choice nonstationary panels with large cross section (N) and time series (T) dimensions. Some results emerging from this theory are directly applicable in the wider context of M-estimation. This includes an extension of work by Wooldridge (1994) on the limit theory of local extremum estimators to multi-indexed processes in nonlinear nonstationary panel data models.

It is shown that the ML estimator is consistent without an incidental parameters problem and has a limit theory with a fast rate of convergence N^{1/2}T^{3/4} (in the stationary case, the rate is N^{1/2}T^{1/2}) for the regression coefficients and thresholds, and a normal limit distribution. In contrast, the limit distribution is known to be mixed normal in time series modeling, as shown in Park and Phillips (2000, hereafter PP), and Phillips, Jin, and Hu (2007, hereafter, PJH).

The approach is applied to exchange rate regime choice by monetary authorities, and we provide an analysis of the empirical phenomenon known as \"fear of floating\".