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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, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 53, pages 119-137.
    • Handle: RePEc:ris:apltrx:0364
    as

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    References listed on IDEAS

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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;
    All these keywords.

    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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