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...

We compare the performance of alternative recursive forecasting models. A simple constant gain algorithm, used widely in the learning literature, both forecasts well out of sample and also provides the best fit to the Survey of Professional Forecasters.

We consider the stability under adaptive learning of the complete set of solutions to the model x_i=beta(Ei*)(x_i+1) when |beat| 1. In addition to the fundamentals solution, the literature describes both finite-state Markov sunspot solutions and autoregressive solutions depending on an arbitrary...

We examine the nonlinear model x_t = E_t F(x_(t+1)). Markov SSEs exist near an indeterminate steady state, hat(x)=F(hat(x)), provided |F'(hat(x)| 1. Despite the importance of indeterminancy in macroeconomics, earlier results have not provided conditions for the existance of adaptively stable...

Expectations play a central role in modern macroeconomic theories. The econometric learning approach models economic agents as forming expectations by estimating and updating forecasting models in real time. The learning approach provides a stability test for rational expectations and a...

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 show that if policy-makers compute the optimal unconstrained interest-rate rule within a Taylor-type class, they may be led to rules that generate indeterminacy and/or instability under learning. This problem is compounded by uncertainty about structural parameters since an optimal rule that...

Forward-looking monetary models with Taylor-type interest rate rules are known to generate indeterminacies, with a potential dependence on extraneous "sunspots," for some structural and policy parameters. We investigate the stability of these solutions under adaptive learning, focusing on...

Earlier studies of the seigniorage inflation model have found that the high-inflation steady state is not stable under adaptive learning. We reconsider this issue and analyze the full set of solutions for the linearized model. Our main focus is on stationary hyperinflationary paths near the...