Econometric Theory

Table of Contents - October 2012 - Volume 28, Issue 05  

Articles

ESTIMATORS FOR PERSISTENT AND POSSIBLY NONSTATIONARY DATA WITH CLASSICAL PROPERTIES

Yuriy Gorodnichenkoa1, Anna Mikushevaa2 and Serena Nga3 c1

a1 University of California at Berkeley

a2 MIT

a3 Columbia University

Abstract

This paper considers a moments-based nonlinear estimator that is $\root \of T $-consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, and certain nonlinear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and so that a central limit theorem can be applied. Critical values from the normal distribution can be used irrespective of the treatment of the deterministic terms. Simulations show that the estimates are precise and the t-test has good size in the parameter region where the least squares estimates usually yield distorted inference.

Correspondence:

c1 Address correspondence to Serena Ng, Department of Economics, Columbia University, 420 W. 118 St., New York, NY 10027, USA; e-mail: Serena.Ng@columbia.edu.

Footnotes

  The authors thank the editor and three referees for many helpful comments and suggestions. Mikusheva acknowledges financial support from the Castle-Krob Career Development Chair. Ng acknowledges financial support from the National Science Foundation (SES-0962431).