Arbeitspapier

Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning

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.

Language: Englisch

Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 10-077/4

Classification: Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Expectations; Speculations

Subject: Adaptive learning
forecast feedback
stochastic approximation
linear regression with stochastic regressors
consistency
Lernen
Rationales Verhalten
Prognoseverfahren
Regression
Stochastischer Prozess
Agentenbasierte Modellierung
Theorie

Event: Geistige Schöpfung

(who): Christopeit, Norbert
Massmann, Michael

Event: Veröffentlichung

(who): Tinbergen Institute

(where): Amsterdam and Rotterdam

(when): 2010

Handle: http://hdl.handle.net/10419/86764

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

Christopeit, Norbert
Massmann, Michael
Tinbergen Institute

Time of origin

2010

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Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning

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