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

Nikita Moiseev () and Andrei Volodin ()
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Nikita Moiseev: Plekhanov Russian University of Economics, Moscow, Russia
Andrei Volodin: University of Regina, Regina, Canada

Applied Econometrics, 2019, vol. 53, 119-137

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.

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 (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
Date: 2019
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