Measures of Fit for Calibrated Models
提出一种新方法，通过向模型变量添加最小随机误差使其完全匹配实际数据的二阶矩，并基于误差大小构建拟合优度度量。应用于标准真实商业周期模型发现，为匹配二战后美国经济数据，模型需添加大量误差。
This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Copyright 1993 by University of Chicago Press.