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

We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.

We evaluate some aspects of the finite sample distribution of an instrumental variables estimator of a first order condition of the Holt et al. (1960) linear quadratic inventory model. We find that for some but not all empirically relevant data generating processes and sample sizes, asymptotic theory predicts a wide dispersion of parameter estimates, with a substantial finite sample probability of estimates with incorrect signs. For such data generating processes, simulation evidence suggests that different choices of left hand side variables often produce parameter estimates of an opposite sign. More generally, while the asymptotic theory often provides a good approximation to the finite sample distribution, sometimes it does not

We evaluate some aspects of the finite sample distribution of an instrumental variables estimator of a first order condition of the linear quadratic inventory model of C. C. Holt, F. Modighiani, J. F. Muth and H. A. Simon [‘Planning production, inventories and work force’ (Englewood Cliffs, 1960)]. We find that for some but not all empirically relevant data generating processes and sample sizes, asymptotic theory predicts a wide dispersion of parameter estimates, with a substantial finite sample probability of incorrectly signed estimates. Simulations indicate that the asymptotic theory usually but not always provides a good approximation to the finite sample distribution.

We compare the out-of-sample forecasting performance of univariate homoskedastic, GARCH, autoregressive, and nonparametric models for conditional variances, using five bilateral weekly exchange rates for the dollar, 1973–1989. For a one-week horizon, GARCH models tend to make slightly more accurate forecasts. For longer horizons, it is difficult to find grounds for choosing between the various models. None of the models perform well in a conventional test of forecast efficiency.

The Environmental Stewardship Scheme provides payments to farmers for the provision of environmental services based on agricultural foregone income. This creates a potential incentive compatibility problem which, combined with an information asymmetry on farm land heterogeneity, could lead to adverse selection of farmers into the scheme. However, the Higher Level Scheme (HLS) design includes some features that potentially reduce adverse selection. This paper studies the adverse selection problem of the HLS using a principal agent framework at the regional level. It is found that, at the regional level, the enrolment of more land from lower payment regions for a given budget constraint has led to a greater overall contracted area (and thus potential environmental benefit) which has had the effect of reducing the adverse selection problem. In addition, for landscape regions with the same payment rate (i.e. of the same agricultural value), differential weighting of the public demand for environmental goods and services provided by agriculture (measured by weighting an environmental benefit function by the distance to main cities) appears to be reflected into the regulator’s allocation of contracts, thereby also reducing the adverse selection problem.

When estimates of variances are used to make asset allocation decisions, underestimates of population variances lead to lower expected utility than equivalent overestimates: a utility based criterion is asymmetric, unlike standard criteria such as mean squared error. To illustrate how to estimate a utility based criterion, we use five bilateral weekly dollar exchange rates, 1973-1989, and the corresponding pair of Eurodeposit rates. Of homoskedastic, GARCH, autoregressive and nonparametric models for the conditional variance of each exchange rate, GARCH models tend to produce the highest utility, on average. A mean squared error criterion also favors GARCH, but not as sharply.

When estimates of variances are used to make asset allocation decisions, underestimates of population variances lead to lower expected utility than equivalent overestimates: a utility-based criterion is asymmetric, unlike standard criteria such as mean squared error. To illustrate how to estimate a utility-based criterion, we use five bilateral weekly dollar exchange rates, 1973–1989, and the corresponding pair of Eurodeposit rates. Of homoskedastic, GARCH, autoregressive and non-parametric models for the conditional variance of each exchange rate, GARCH models tend to produce the highest utility, on average. A mean squared error criterion also favors GARCH, but not as sharply.

A simple real model is used to decompose movements of aggregate inventories and output in Japan during 1975 to 1987 to three components, one due to cost shocks, one due to demand shocks, and one due to' shocks from abroad. Cost shocks are estimated to account for about one tenth of the movement in GNP, one half of the movement in inventories. Most of the remaining movement in GNP is due to demand shocks, in inventories to shocks from abroad. Confidence intervals around these point estimates are, however, very large.