Arbeitspapier

Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning

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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.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. 10-077/4

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

Ereignis
Geistige Schöpfung
(wer)
Christopeit, Norbert
Massmann, Michael
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2010

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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Objekttyp

  • Arbeitspapier

Beteiligte

  • Christopeit, Norbert
  • Massmann, Michael
  • Tinbergen Institute

Entstanden

  • 2010

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