Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86764
Authors: 
Christopeit, Norbert
Massmann, Michael
Year of Publication: 
2010
Series/Report no.: 
Tinbergen Institute Discussion Paper 10-077/4
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.
Subjects: 
Adaptive learning
forecast feedback
stochastic approximation
linear regression with stochastic regressors
consistency
JEL: 
C13
C22
D83
D84
Document Type: 
Working Paper

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