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Empirical Best Linear Unbiased Prediction in Misspecified and Improved Panel Data Models with an Application to Gasoline Demand

Author

Listed:
  • I-Lok Chang
  • P.A.V.B. Swamy
  • Yaghi Wisam

Abstract

We emphasize using our solutions to the problems of omitted variables, measurement errors, and unknown functional forms to improve model specification, and to estimate the mean square error of an empirical best linear unbiased predictor of an individual drawing of the dependent variable of an improved model. We illustrate using data to compare the forecasting performances of misspecified and improved models of the U.S. gasoline market. The performance criterion used is the tightness of the distribution of the absolute relative errors in out-of-sample multi-step-ahead forecasts around zero. The results show that significant improvements in forecasting accuracy can be obtained by improving the specifications of misspecified models. Numerical algorithms for generating forecasts from a rolling forecast method are presented

Suggested Citation

  • I-Lok Chang & P.A.V.B. Swamy & Yaghi Wisam, 2005. "Empirical Best Linear Unbiased Prediction in Misspecified and Improved Panel Data Models with an Application to Gasoline Demand," Computing in Economics and Finance 2005 26, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:26
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    More about this item

    Keywords

    Omitted variables; Measurement errors; Unknown functional forms; Stochastic coefficients; Panel data; Forecast comparisons.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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