Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning
AbstractIn 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.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-077/4.
Date of creation: 23 Aug 2010
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Adaptive learning; forecast feedback; stochastic approximation; linear regression with stochastic regressors; consistency;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-09-11 (All new papers)
- NEP-CBA-2010-09-11 (Central Banking)
- NEP-ECM-2010-09-11 (Econometrics)
- NEP-FOR-2010-09-11 (Forecasting)
- NEP-ORE-2010-09-11 (Operations Research)
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