This paper deals with macroeconomic aspects of widespread substance abuse with a reference to illicit drugs as an example. Substance abuse impedes the productivity of the labor force and reduces economic growth. Workers are either nonusers and therefore fully productive, a number of whom are employed by the government in drug-control activities, or users who are only partially productive. Efficient management of the nation's portfolio of workers involves eradicating drug use when initial user numbers are lower than a critical level, but allows user numbers to rise to, and be accommodated at, a stationary level when initial user numbers exceed a critical level.

There is now a substantial theoretical literature arguing that inflation impedes financial deepening. Furthermore, it has been hypothesized that the relationship is a nonlinear one, in that there is a threshold level of inflation below which inflation has a positive effect on financial depth, but above which the effect turns negative. Using a large cross-country sample, empirical support is found for the existence of such a threshold. The estimates indicate that the threshold level of inflation is generally about 3–6 percent per annum, depending on the specific measure of financial depth that is utilized.

This paper studies asset returns adopting an alternative strategy to assess a model's goodness of fit. Based on spectral analysis, this approach considers a model as an approximation to the process generating the observed data, and characterizes the dimensions for which the model provides a good approximation and those for which it does not. Our aim is to offer new evidence regarding the size and the location of approximation errors of a set of stochastic growth models considered to be decisive steps in the progress of the asset pricing research program. Our specific objective is to reevaluate the results of Jermann's (1998) model extending the calculations to the spectral domain. Spectral results are relatively satisfactory: the benchmark model needs very few contributions of approximation errors to account for the empirical equity premium. Second, the location of the approximation errors, when they are substantial, seems to be essentially concentrated at high frequencies.

Progress in stochastic macroeconomic modeling justifies revisiting Milton Friedman's program on the relation between macroeconomic stability and active stabilization policies. In the lecture, we use a standard new Keynesian model but depart from rational expectations by assuming that agents behave in line with adaptive learning, which increase the potential for instability in the economy.

Optimal policy under adaptive learning displays some similarity with optimal policy under commitment in the rational expectations setting. Specifically, we find that optimal policy responds in a persistent manner when expectations threaten to become unhinged. Finally, we illustrate the dynamics associated with the change from a simple regime that ignores the expectation formation, to the optimal policy that does. The results are not unlike the behavior of the U.S. economy around the Volcker transition (October 1979).

We consider two kinds of answers to the title question: Do random shifts in monetary policy account for historical recessions, and would changes in the systematic component of monetary policy have allowed reductions in inflation or output variance without substantial costs. The answer to both questions is no. We use weak identifying assumptions and include extensive discussion of these assumptions, including a completely specified dynamic stochastic equilibrium model in which our identifying assumptions can be shown to be approximately satisfied.

This paper provides a closed-form solution to a standard asset pricing model with habit formation when the growth rate of endowment follows a first-order Gaussian autoregressive process. We determine conditions that guarantee the existence of a stationary bounded equilibrium. The findings are useful because they allow to evaluate the accuracy of various approximation methods to nonlinear rational expectation models. Furthermore, they can be used to perform simulation experiments to study the finite sample properties of various estimation methods.