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This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels. Keywords Simul...

Abstract: This paper uses a simple New Keynesian monetary DSGE model as a prior for a vector autoregression and shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis. JEL classification: C11, C32, C53

Reserve Board, and George Washington University, as well as those at the Midwest

helpful comments, and the National Science Foundation and the Graduate School We develop regression-based tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevant environments are ones in which predictions depend on estimated parameters. We show that standard regression statistics generally fail to account for error introduced by estimation of these parameters. We propose computationally convenient test statistics that properly account for such error. Simulations indicate that the procedures can work well in samples of size typically available, although there sometimes are substantial size distortions.

Predicting economic activity is important for numerous reasons. It is important for business firms because it aids in deciding how much capacity will be needed to meet future demand. It is important for various government agencies when forecasting budgetary surpluses or deficits. And it is important for the Federal Reserve (the Fed) in deciding the stance of current monetary policy. One set of variables that are potentially useful in forecasting economic activity are financial variables. Financial market participants are forward-looking, and as a result the prices of various securities embody expectations of future economic activity. This pricing behavior implies that data from financial markets may reasonably be expected to help forecast the growth rate of the economy. Using financial variables to aid in economic projections, therefore, is fairly commonplace. In particular, the yield curve spread between long- and short-term interest rates has received a lot of recent attention. Although not the first to consider the implications that the spread has for predicting economic activity, Stock and

We study the long-run relationship between nominal exchange rates and monetary fundamentals in a quarterly panel of 18 countries extending from 1973.1 to 1997.1. Our analysis is centered on two issues. First, we test whether exchange rates are cointegrated with long-run determinants predicted by economic theory. These results generally support the hypothesis of cointegration. The second issue is to re-examine the ability for monetary fundamentals to forecast future exchange rate returns. Panel regression estimates and forecasts con¯rm that this forecasting power is signi¯cant. 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.