Electronic Journal of Statistics

Strong consistency of the least squares estimator in regression models with adaptive learning

Norbert Christopeit and Michael Massmann

Full-text: Open access

Abstract

This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear regression models with adaptive learning. It is a companion to Christopeit & Massmann (2018) which considers the estimator’s convergence in distribution and its weak consistency in the same setting. Under constant gain learning, the model is closely related to stationary, (alternating) unit root or explosive autoregressive processes. Under decreasing gain learning, the regressors in the model are asymptotically collinear. The paper examines, first, the issue of strong convergence of the learning recursion: It is argued that, under constant gain learning, the recursion does not converge in any probabilistic sense, while for decreasing gain learning rates are derived at which the recursion converges almost surely to the rational expectations equilibrium. Secondly, the paper establishes the strong consistency of the OLS estimators, under both constant and decreasing gain learning, as well as rates at which the estimators converge almost surely. In the constant gain model, separate estimators for the intercept and slope parameters are juxtaposed to the joint estimator, drawing on the recent literature on explosive autoregressive models. Thirdly, it is emphasised that strong consistency is obtained in all models although the near-optimal condition for the strong consistency of OLS in linear regression models with stochastic regressors, established by Lai & Wei (1982a), is not always met.

Article information

Source
Electron. J. Statist., Volume 13, Number 1 (2019), 1646-1693.

Dates
Received: August 2018
First available in Project Euclid: 17 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1555466480

Digital Object Identifier
doi:10.1214/19-EJS1558

Zentralblatt MATH identifier
07056160

Subjects
Primary: 62F10: Point estimation 62H12: Estimation 62J05: Linear regression 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 91A26: Rationality, learning 91B64: Macro-economic models (monetary models, models of taxation) 91B84: Economic time series analysis [See also 62M10]

Keywords
Adaptive learning almost sure convergence non-stationary regression ordinary least squares

Rights
Creative Commons Attribution 4.0 International License.

Citation

Christopeit, Norbert; Massmann, Michael. Strong consistency of the least squares estimator in regression models with adaptive learning. Electron. J. Statist. 13 (2019), no. 1, 1646--1693. doi:10.1214/19-EJS1558. https://projecteuclid.org/euclid.ejs/1555466480


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