The authors show how to use security market data to restrict the admissible region for means and standard deviations of intertemporal marginal rates of substitution of consumers. Their approach (1) is nonparametric and applies to a rich class of models of dynamic economics; (2) characterizes the duality between the mean-standard deviation frontier for intertemporal marginal rates of substitution and the familiar mean-standard deviation frontier for asset returns; and (3) exploits the restriction that intertemporal marginal rates of substitution are positive random variables. The region provides a convenient summary of the sense in which asset market data are anomalous from the vantage point of intertemporal asset pricing theory.

This paper studies the time-series behavior of asset returns and aggregate consumption. Using a representative consumer model and imposing restrictions on preferences and the joint distribution of consumption and returns, we deduce a restricted log-linear time-series representation. Preference parameters for the representative agent are estimated and the implied restrictions are tested using postwar data.

This paper examines the hypothesis that the expected rate of return to speculation in the forward foreign exchange market is zero; that is, the logarithm of the forward exchange rate is the market's conditional expectation of the logarithm of the future spot rate. A new computationally tractable econometric methodology for examining restrictions on a k-step-ahead forecasting equation is employed. Using data sampled more finely than the forecast interval, we are able to reject the simple market efficiency hypothesis for exchange rates from the 1970s and the 1920s. For the modern experience, the tests are also inconsistent with several alternative hypotheses which typically characterize the relationship between spot and forward exchange rates.

In this article we characterize and estimate the process for short-term interest rates using federal funds interest rate data. We presume that we are observing a discrete-time sample of a stationary scalar diffusion. We concentrate on a class of models in which the local volatility elasticity is constant and the drift has a flexible specification. To accommodate missing observations and to break the link between "economic time" and calendar time, we model the sampling scheme as an increasing process that is not directly observed. We propose and implement two new methods for estimation. We find evidence for a volatility elasticity between one and one-half and two. When interest rates are high, local mean reversion is small and the mechanism for inducing stationarity is the increased volatility of the diffusion process.

This paper investigates empirically a model of aggregate consumption and leisure decisions in which utility from goods and leisure is nontime-separable. The nonseparability of preferences accommodates intertemporal substitution or complementarity of leisure and thereby affects the comovements in aggregate compensation and hours worked. These cross-relations are examined empirically using postwar monthly U. S. data on quantities, real wages, and the real return on the one-month Treasury bill. The estimated values of the parameters governing preferences differ significantly from the values assumed in several studies of real business models. Several possible explanations of these discrepancies are discussed.

This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It focuses on rational specifications of spectral density matrices since rational parameterizations are commonly employed in the analysis of the time series data.

Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write highly stylized damage functions to speculate about how climate change alters macroeconomic and growth opportunities. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components to a revealing quantitative illustration.

This paper describes links between the max-min expected utility theory of Gilboa and Schmeidler (1989) and the applications of robust control theory proposed by Anderson, Hansen and Sargent (2000) and Dupuis, James and Petersen (1998).

“… I suppose there exists an extremely powerful, and, if I may so speak, malignant being, whose whole endeavours are directed toward deceiving me.” Rene Descartes, Meditations, II.1

In this article we provide econometric tools for the evaluation of intertemporal asset pricing models using specification-error and volatility bounds. We formulate analog estimators of these bounds, give conditions for consistency, and derive the limiting distribution of these estimators. The analysis incorporates market frictions such as short-sale constraints and proportional transactions costs. Among several applications we show how to use the methods to assess specific asset pricing models and to provide non-parametric characterizations of asset pricing anomalies.