Template-Type: ReDIF-Paper 1.0
Title: OLS--Learning in Non-Stationary Models with Forecast Feedback
Author-Name: Markus Zenner
Abstract: In this study we consider a linear model with forecast feedback in
  	which boundedly rational agents are learning the parameter values of
  	the rational expectations equilibrium by the OLS learning procedure.
  	We show strong consistency of the OLS estimates under much weaker
  	assumptions on the involved time series than the ones usually
  	employed. This result extends the boundedly rational learning
  	approach to models including non-stationary time series, like
  	processes with polynomial trends or unit root autoregressive
  	processes, and indicates that the idea that agents can learn only
  	stationary rational expectations equilibria is misleading.
Keywords: Rational expectations equilibrium, boundedly rational learning,
  	stochastic approximation, non-stationary time series
Classification-JEL: C22; C40; C62; D83
Series: SFB
Number: 303, Discussion Paper B-315
Creation-Date: 1995-05
Length: 24
File-URL: http://www.wiwi.uni-bonn.de/bgsepapers/bonsfb/bonsfb315.ps
File-Format: Application/PostScript
Handle: RePEc:bon:bonsfb:315