Analysis of the use of vector autoregressions in economic forecasting

Abstract

Using vector autoregressions is a promising direction in short-term economic forecasting. They do not simply model the relationship between different factors, but also model the time-distributed relationship of these factors. Vector autoregressions are suitable for modeling complex dynamic economic multifactor processes. The complexity of the problem of estimating coefficients, which increases with the dimensionality of vectors, prevents the widespread use of autoregressions in practice. Vector autoregressions in complex-valued form with the same dimensionality as the modeled vector contain a much smaller number of coefficients. This facilitates the estimation of the coefficients of vector autoregressions. Some problems requiring further investigation arise when using vector autoregressions in complex form. Among them is the problem of selecting the best model. The information criteria used for this purpose limit the variety of vector autoregressions, reducing them to elementary models. The study was supported by the Russian Science Foundation grant No. 23-28-01213, https://rscf.ru/project/23-28-01213.