Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2013-09-26
Number: 13-151/III
Author-Name: Norbert Christopeit
Author-Workplace-Name: University of Bonn, Germany
Author-Name: Michael Massmann
Author-Workplace-Name: VU University Amsterdam
Title: A Note on an Estimation Problem in Models with Adaptive Learning
Abstract: This paper provides an example of a linear regression model with predetermined stochastic regressors for which the sufficient condition for strong consistency of the ordinary least squares estimator by Lai & Wei (1982, Annals of Statistics) is not met. Nevertheless, the estimator is strongly consistent, as shown in a companion paper, cf. Christopeit & Massmann (2013b). This is intriguing because the Lai & Wei condition is the best currently available and is referred to as “in some sense the weakest possible”. Moreover, the example discussed in this paper arises naturally in a class of macroeconomic models with adaptive learning, the estimation of which has recently gained popularity amongst researchers and policy makers.
Classification-JEL: C22, C51, D83
Keywords: least-squares regression, stochastic regressors, strong consistency, minimal sufficient condition, adaptive learning
File-Url: https://papers.tinbergen.nl/13151.pdf
File-Format: application/pdf
File-Size: 424846 bytes
Handle: RePEc:tin:wpaper:20130151