We show that the stock market may fail to aggregate information even if it appears to be efficient, and that the resulting decrease in the information content of prices may drastically reduce welfare. We solve a macroeconomic model in which information about fundamentals is dispersed and...

We develop a novel, risk-based theory of the effects of currency manipulation. In our model, the choice of exchange rate regime allows policymakers to make their currency, and by extension, the firms in their country, a safer investment for international investors. Policies that induce a...

We introduce the information microstructure of a canonical noisy rational expectations model (Hellwig, 1980) into the framework of a conventional real business cycle model. Each household receives a private signal about future productivity. In equilibrium, the stock price serves to aggregate and...

We investigate the link between stochastic properties of exchange rates and differences in capital-output ratios across industrialized countries. To this end, we endogenize capital accumulation within a standard model of exchange rate determination with nontraded goods. The model predicts that...

In conventional stochastic simulation algorithms, Monte Carlo integration and curve fitting are merged together and implemented by means of regression. We perform a decomposition of the solution error and show that regression does a good job in curve fitting but a poor job in integration, which...

We use the stochastic simulation algorithm, described in Judd, Maliar and Maliar (2009), and the cluster-grid algorithm, developed in Judd, Maliar and Maliar (2010a), to solve a collection of multi-country real business cycle models. The following ingredients help us reduce the cost in...

We introduce a technique called "precomputation of integrals" that makes it possible to compute conditional expectations in dynamic stochastic models in the initial stage of the solution procedure. This technique can be applied to any set of equations that contains conditional expectations, in...

Continuous-time stochastic games with a finite number of states have substantial computational and conceptual advantages over the more common discrete-time model. In particular, continuous time avoids a curse of dimensionality and speeds up computations by orders of magnitude in games with more...

Numerical dynamic programming algorithms typically use Lagrange data to approximate value functions over continuous states. Hermite data is easily obtained from solving the Bellman equation and can be used to approximate value functions. We illustrate this method with one-, three-, and...

We introduce an algorithm for solving dynamic economic models that merges stochastic simulation and projection approaches: we use simulation to approximate the ergodic measure of the solution, we construct a fixed grid covering the support of the constructed ergodic measure, and we use...