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Improvement of Regression Forecasting Models

Author

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  • Vasiliy Zubakin
  • Oleg Kosorukov
  • Nikita Moiseev

Abstract

In this paper authors propose the technique, which decreases average forecast error of regression based models. The main idea of the method is to use the weighted sum of several regression equations, which satisfy Ordinary Least Squares prerequisites and have independent residuals, instead of only one. It is shown that if all method requirements are met, it is possible to decrease Mean Squared Error almost by half, using just three equations. This technique allows deriving equations which contain more predictors than the number of observations. Additionally, this method proves to be more consistent in time than any of regressions, used in it, separately. It is also illustrated, that the proposed method outperforms the regression equation, computed with the same independent variables, and, thus, it gives more accurate estimators of regression coefficients. Empirical results are provided as well.

Suggested Citation

  • Vasiliy Zubakin & Oleg Kosorukov & Nikita Moiseev, 2015. "Improvement of Regression Forecasting Models," Modern Applied Science, Canadian Center of Science and Education, vol. 9(6), pages 344-344, June.
    • Handle: RePEc:ibn:masjnl:v:9:y:2015:i:6:p:344
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    References listed on IDEAS

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    1. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    2. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    3. Rahim Alhamzawi & Keming Yu, 2012. "Variable selection in quantile regression via Gibbs sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 799-813, August.
    4. Giordani, Paolo & Villani, Mattias, 2010. "Forecasting macroeconomic time series with locally adaptive signal extraction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 312-325, April.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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