Arbeitspapier

Estimating Structural Parameters in Regression Models with Adaptive Learning

This paper investigates the asymptotic properties of the ordinary least squares (OLS) estimator of structural parameters in a stylised macroeconomic model in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous variable. In particular, when the learning recursion is subject to so-called decreasing gain sequences the model does not satisfy, in general, any of the sufficient conditions for consistent estimability available in the literature. The paper demonstrates that, for appropriate parameter sets, the OLS estimator nevertheless remains strongly consistent and asymptotically normally distributed.

Language
Englisch

Bibliographic citation
Series: Tinbergen Institute Discussion Paper ; No. 13-111/III

Classification
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Subject
non-stationary regression
strong consistency
asymptotic normality
bounded rationality
adaptive learning

Event
Geistige Schöpfung
(who)
Christopeit, Norbert
Massmann, Michael
Event
Veröffentlichung
(who)
Tinbergen Institute
(where)
Amsterdam and Rotterdam
(when)
2013

Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Christopeit, Norbert
  • Massmann, Michael
  • Tinbergen Institute

Time of origin

  • 2013

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