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Increasing the accuracy of macroeconomic time series forecast by incorporating functional and correlational dependencies between them

Author

Listed:
  • Moiseev, Nikita

    () (Plekhanov Russian University of Economics, Moscow, Russia)

  • Volodin, Andrei

    () (University of Regina, Regina, Canada)

Abstract

The paper presents a parametric approach to forecasting vectors of macroeconomic indicators, which takes into account functional and correlation dependencies between them. It is asserted that this information allows to achieve a steady decrease in their mean-squared forecast error. The paper also provides an algorithm for calculating the general form of the corrected probability density function for each of modelled indicators. In order to prove the efficiency of the proposed method we conduct a rigorous simulation and empirical investigation.

Suggested Citation

  • Moiseev, Nikita & Volodin, Andrei, 2019. "Increasing the accuracy of macroeconomic time series forecast by incorporating functional and correlational dependencies between them," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 53, pages 119-137.
  • Handle: RePEc:ris:apltrx:0364
    as

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    File URL: http://pe.cemi.rssi.ru/pe_2019_53_119-137.pdf
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    References listed on IDEAS

    as
    1. Ing, Ching-Kang & Wei, Ching-Zong, 2003. "On same-realization prediction in an infinite-order autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 130-155, April.
    2. Antoniadis, Anestis & Sapatinas, Theofanis, 2007. "Estimation and inference in functional mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4793-4813, June.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    4. Wensheng Guo, 2002. "Functional Mixed Effects Models," Biometrics, The International Biometric Society, vol. 58(1), pages 121-128, March.
    5. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    6. Huaihou Chen & Yuanjia Wang, 2011. "A Penalized Spline Approach to Functional Mixed Effects Model Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 861-870, September.
    7. Bruce E. Hansen, 2014. "Model averaging, asymptotic risk, and regressor groups," Quantitative Economics, Econometric Society, vol. 5(3), pages 495-530, November.
    8. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(2), pages 254-279, April.
    9. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    10. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
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    More about this item

    Keywords

    Regression analysis; GDP; Inflation; Monetary base; Unemployment; Maximum likelihood method; Probability density function; Functional and correlation dependencies of macroeconomic indicators; Projection accuracy; Mean square error; Bayesian econometrics;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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