Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2010-08-23
Number: 10-077/4
Author-Name: Norbert Christopeit
Author-Workplace-Name: University of Bonn
Author-Name: Michael Massmann
Author-Workplace-Name: VU University Amsterdam
Title: Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning
Abstract: In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic approximation algorithms whose gain sequence is decreasing to zero. Our focus is on the estimation of the parameters in the resulting actual law of motion. For a special case we show that the ordinary least squares estimator is consistent.
Classification-JEL: C13, C22, D83, D84
Keywords: Adaptive learning, forecast feedback, stochastic approximation, linear regression with stochastic regressors, consistency
File-Url: https://papers.tinbergen.nl/10077.pdf
File-Format: application/pdf
File-Size: 312814 bytes
Handle: RePEc:tin:wpaper:20100077