Research Database

No abstract available.

This paper catalogues formulas that are useful for estimating dynamic linear economic models. We describe algorithms for computing equilibria of an economic model and for recursively computing a Gaussian likelihood function and its gradient with respect to parameters. We display an application to Rosen, Murphy, and Scheinkman's (1994) model of cattle cycles.

Using a novel meta-analytical method, Brodeur et al. (2016) argue that hypothesis tests in top economic journals have exaggerated levels of statistical significance. Brodeur et al. (2020) apply the same method to another sample of hypothesis tests, obtaining similar results. We investigate the reliability of the method by highlighting questionable assumptions and compiling a dataset to examine their merits. Our findings support the original conclusions.

No abstract available.

Silberberg [6] and Pauwels [2] have produced and clarified seminal results in the comparative statics of single-agent classical optimization problems. This paper extends Pauwels’ method to derive analogous results for stable Nath equilibria in a subclass of the widely used class of concave orthogonal games defined by Rosen [3]. Application of these results to cost curve shifts in the asymmetric Cournot oligopoly immediately uncovers apparently new comparative statics results.

In “The Inefficiency of Interest-Bearing National Debt,” (JPE, April 1979) we argued that private sector transaction costs are needed in order to explain interest on government debt. It follows that if the government’s transaction costs do not depend on its portfolio, then, barring special circumstances, an open-market purchase is deflationary and welfare improving. In this paper we show that this result can survive a potentially relevant special circumstance: reserve requirements which limit the size of insured intermediaries.

This paper derives a variance bounds test for a broad class of linear rational expectations models. According to this test, if observed data accord with the model, then a weighted sum of auto-covariances of the covariance-stationary components of the endogenous state variables should be nonnegative. The new test reinterprets West’s (1986) variance bounds test and extends its applicability by not requiring observable exogenous state variables, covariance-stationary exogenous or endogenous state variables, or a zero initial value for the endogenous state variable. The paper also discusses the possibility of the new test’s application to nonlinear models.

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.

This paper proposes a method for estimating the parameters of continuous time, stochastic rational expectations models from discrete time observations. The method is important since various heuristic procedures for deducing the implications for discrete time data of continuous time models, such as replacing derivatives with first differences, can sometimes give rise to very misleading conclusions about parameters. Our proposal is to express the restrictions imposed by the rational expectations model on the continuous time process generating the observable variables. Then the likelihood function of a discrete time sample of observations from this process is obtained. Parameter estimates are computed by maximizing the likelihood function with respect to the free parameters of the continuous time model.