This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation

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Abstract. This paper presents a taxonomy of existing approaches for using response surfaces for global optimization. Each method is illustrated with a simple numerical example that brings out its advantages and disadvantages. The central theme is that methods that seem quite reasonable often have non-obvious failure modes. Understanding these failure modes is essential for the development of practical algorithms that fulfill the intuitive promise of the response surface approach. Key words: global optimization, response surface, kriging, splines 1.

We consider using out-of-sample mean squared prediction errors (MSPEs) to evaluate the null that a given series follows a zero mean martingale difference against the alternative that it is linearly predictable. Under the null of no predictability, the population MSPE of the null “no change ” model equals that of the linear alternative. We show analytically and via simulations that despite this equality, the alternative model’s sample MSPE is expected to be greater than the null’s. For rolling regression estimators of the alternative model’s parameters, we propose and evaluate an asymptotically normal test that properly accounts for the upward shift of the sample MSPE of the alternative model. Our simulations indicate that our proposed procedure works well.

This article describes how and why official U.S. estimates of the growth in real economic output and inflation are revised over time, demonstrates how big those revisions tend to be, and evaluates whether the revisions matter for researchers trying to understand the economy's performance and the contemporaneous reactions of policymakers. The conclusion may seem obvious, but it is a point ignored by most researchers: To have a good chance of understanding how policymakers make their decisions, researchers must use not the final data available, but the data available initially, when the policy decisions are actually made. The views expressed herein are those of the author and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System. During 1974 and 1975, the U.S. economy reeled from the effects of huge oil price increases. At that time, data suggested that the economy was in a recession, a recession by far more severe than any since shortly...

This paper integrates and extends some recent computational advances in Bayesian inference with the objective of more fully realizing the Bayesian promise of coherent inference and model comparison in economics. It combines Markov chain Monte Carlo and independence Monte Carlo with importance sampling to provide an efficient and generic method for updating posterior distributions. It exploits the multiplicative decomposition of marginalized likelihood into predictive factors, to compute posterior odds ratios efficiently and with minimal further investment in software. It argues for the use of predictive odds ratios in model comparison in economics. Finally, it suggests procedures for public reporting that will enable remote clients to conveniently modify priors, form posterior expectations of their own functions of interest, and update the posterior distribution with new observations. A series of examples explores the practicality and efficiency of these methods.

Faced with the question whether the Belgian failure prediction models by Ooghe, Joos and De Vos (1991) can be easily applied in all industries and for all sizeclasses, this study compares the performance of the OJD models across 18 different industries and different sizeclasses. After a brief theoretical review of the logistic regression modelling technique, which was used to design the OJD 1991 models, and the performance measures that are used to evaluate these models, we report type I and type II error rates corresponding with the original cut-off points of the models. Furthermore, we calculate new optimal cut-off points, as well as Ginicoefficients. Finally we report the reductions in unweighted error rates when using the new cut-off points instead of the original ones, and the graphs of the trade-off functions. As can be concluded from the performance results and the trade-off functions, there’s a wide range of performances for the different industries. However, we notice that the OJD 1991 models perform best for classical manufacturing industries- such as chemicals, paper and printing, textiles and apparel, paper and printing and metal- and financial services., while the models show the worst performance for service industries- such as real estate, hotel,

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Estimates of standard performance measures can be improved by using returns on assets not used to dene those measures. Alpha, the intercept in a regression of a fund's return on passive benchmark returns, can be estimated more precisely by using information in returns on non-benchmark passive assets, whether or not one believes those assets are priced by the benchmarks. A fund's Sharpe ratio can be estimated more precisely by using returns on other assets as well as the fund. New estimates of these performance measures for a large universe of equity mutual funds exhibit substantial differences from the usual estimates.

paper was also partly written at the Centre de recherche en Économie et Statistique (INSEE, Paris) and the Technische