We discuss some practical issues related to the use of the Parameterized Expectations Approach (PEA) for solving non-linear stochastic dynamic models with rational expectations. This approach has been applied in models of macroeconomics, financial economics, economic growth, contract theory,...

Many empirical studies of business cycles have followed the practise of applying the Hodrick-Prescott filter for cross-country comparisons. The standard procedure is to set the weight \lambda, which determines the 'smoothness' of the trend equal to 1600. We show that if this value is used for...

This paper uses a model of boundedly rational learning to account for the observations of recurrent hyperinflations in the last decade. We study a standard monetary model where the fully rational expectations assumption is replaced by a formal definition of quasi-rational learning. The model...

We obtain a recursive formulation for a general class of contracting problems involving incentive constraints. Under these constraints, the corresponding maximization (sup) problems fails to have a recursive solution. Our approach consists of studying the Lagrangian. We show that, under standard...

To recover a version of Barro's (1979) `random walk' tax smoothing outcome, we modify Lucas and Stokey's (1983) economy to permit only risk--free debt. This imparts near unit root like behavior to government debt, independently of the government expenditure process, a realistic outcome in the...

We study the issue of income convergence across countries and regions with a Bayesian estimator which allows us to use information in an efficient and flexible way. We argue that the very slow convergence rates to a common level of per-capita income found, e.g., by Barro and Xavier...

This paper studies the short run correlation of inflation and money growth. We study whether a model of learning can do better than a model of rational expectations, we focus our study on countries of high inflation. We take the money process as an exogenous variable, estimated from the data...