This paper develops a simple two-country, two-good model of international trade and borrowing that suppresses all previous sources of current account dynamics. Under rational expectations, international debt follows a random walk. Under adaptive learning however, international debt behaves like...

This paper addresses the output-price volatility puzzle by studying the interaction of optimal monetary policy and agents' beliefs. We assume that agents choose their information acquisition rate by minimizing a loss function that depends on expected forecast errors and information costs....

We propose behavioral learning equilibria, where boundedly rational agents learn to use a simple univariate linear forecasting rule with correctly specified unconditional mean and first-order autocorrelation. In the long run, agents learn the best univariate linear forecasting rule, without...

Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the...

This paper models expectation formation by taking into account that agents may produce heterogeneous expectations because of informational frictions and differing levels of a capacity to process information. We show that there are two general classes of steady states within this framework: those...

We extend the correspondences between adaptive learning algorithms and the Kalman filter to formulations with time-varying gains. Our correspondences hold exactly, in a computational implementation sense, and we discuss how they relate to previous approximate correspondences found in the literature.

In this paper we study the power of direct tests for rational expectations against the constant gain learning alternative. The investigation is by means of a Monte Carlo study. The tests considered use quantitative expectations data and qualitative survey data that has been quantified. The main...

This paper examines the government spending multiplier when economic agents form their expectations based on an adaptive learning scheme. The learning mechanism is such that the agents forecast future values of forward-looking variables using a linear function of an information set that does not...

Abstract: This paper models expectation formation by taking into account that agents produce heterogeneous expectations due to model uncertainty, informational frictions and different capacities for processing information. We show that there are two general classes of steady states within this...

We examine the role of generalized constant gain stochastic gradient (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat tailed...