Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 13, Number 1 (2019), 1646-1693.
Strong consistency of the least squares estimator in regression models with adaptive learning
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.
Electron. J. Statist., Volume 13, Number 1 (2019), 1646-1693.
Received: August 2018
First available in Project Euclid: 17 April 2019
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
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]
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