Kenneth D. West’s research while affiliated with University of Wisconsin–Madison and other places

A simple argument is used to derive the optimal GMM estimator of a finite order autoregressive process whose innovation may be conditionally heteroskedastic.

We consider the contribution to the analysis of economic time series of the generalized method-of-moments estimator introduced by Hansen. We outline the theoretical contribution, conduct a small-scale literature survey, and discuss some ongoing theoretical research.

All economic agents forecast, and forecasting figures especially prominently in financial and macroeconomic contexts. Central to finance, for example, is the idea of expectations of earnings streams and their effects on asset prices, and central to macroeconomics is the idea of expectations of business conditions and their effects on investment and consumption decisions. Moreover, predictive ideas are very much intertwined in finance and macroeconomics. Modern asset pricing models, for example, attribute excess returns and return predictability in part to macroeconomic factors such as recession risk. There is a strong derived demand for development and assessment of econometric methods for use in empirical finance and macroeconomics, with special emphasis on problems of prediction, which is the focus of a working group led by us with generous support from the National Bureau of Economic Research and the National Science Foundation. On the finance side, recent years have featured extensive inquiry into issues such as long-horizon mean reversion in asset returns, persistence in mutual fund performance, volatility and correlation forecasting with applications to financial risk management, and selection biases due to survival or data snooping. Similarly, on the macroeconomics side, we have seen the development and application of new coincident and leading indicators, diffusion indexes, regime-switching models (with potentially time-varying transition probabilities), and new breeds of macroeconomic models that demand new tools for estimation and forecasting.

In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in estimation of a linear model with a disturbance that is serially uncorrelated and conditionally heteroskedastic.

This paper presents analytical and simulation results on the properties of two tests for forecast encompassing, allowing throughout for dependence of the forecasts on estimated regression parameters. One test, which was intended for forecasts that do not depend on regression parameters, was developed by Harvey, Leyboume and Newbold (1998). This test works relatively well when the size of the sample of forecast errors is small. A second test, which explicitly accounts for uncertainty about the regression parameters, otherwise is comparable or preferable.

This paper considers regression-based tests for encompassing, when none of the models under consideration encompasses all the other models. For both in- and out-of-sample applications, I derive asymptotic distributions and propose feasible procedures to construct confidence intervals and test statistics. Procedures that are asymptotically valid under the null of encompassing (e.g., Davidson and MacKinnon, Econometrica 49 (1981) 781) can have large asymptotic and finite sample distortions. Simulations indicate that the proposed procedures can work well in samples of size typically available, though the divergence between actual and nominal confidence interval coverage sometimes is large.

We review and interpret recent work on inventories, emphasizing empirical and business cycle aspects. We begin by documenting two empirical regularities about inventories. The first is the well-known one that inventories move procyclically. The second is that inventory movements are quite persistent, even conditional on sales.To consider explanations for the two facts, we present a linear-quadratic model. The model can rationalize the two facts in a number of ways, but two stylized explanations have the virtue of relative simplicity and support from a number of papers. Both assume that there are persistent shocks to demand for the good in question, and that marginal production cost slopes up. The first explanation assumes as well that there are highly persistent shocks to the cost of production. The second assumes that there are strong costs of adjusting production and a strong accelerator motive.Research to date, however, has not reached a consensus on whether one of these two, or some third, alternative provides a satisfactory explanation of inventory behavior. We suggest several directions for future research that promise to improve our understanding of inventory behavior and thus of business cycles.