Estimating Structural Parameters in Regression Models with Adaptive Learning - Norbert Christopeit; Michael Massmann (Tinbergen Institute, 2013)
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.
Keywords: C22; C51; D83; 330; non-stationary regression; strong consistency; asymptotic normality; bounded rationality; adaptive learning