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Bootstrapping an Econometric Model: Some Empirical Results
Authors:David A. Freedman  Stephen C. Peters
Affiliation:1. Statistics Department , University of California , Berkeley , CA , 94720;2. Center for Computational Research in Economics and Management Science, MIT , Cambridge , MA , 02139
Abstract:The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation, based on a nonparametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric model describing the demand for capital, labor, energy, and materials. The model is fitted by three-stage least squares. In sharp contrast with previous results, the coefficient estimates and the estimated standard errors perform very well. However, the model's forecasts show serious bias and large random errors, significantly understated by the conventional standard error of forecast.
Keywords:Regression  Generalized least squares  Two-stage least squares  Three-stage least squares  Econometric models  Forecasting  Standard errors
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