Аннотация
Any theory is based on a certain axiomatic core, which includes axioms and postulates. The latter includes conclusions and results from other theories or branches of science that are accepted in this theory without proof. Among such postulates accepted in modern economic forecasting are informational criteria, which are used to select the best forecasting model from a set of competing ones. Most often, forecasters use two main criteria — Akaike and Schwarz. The article demonstrates, using the example of short-term forecasting of 120 different data series through AR(p) autoregressions, that in practice this tool does not perform as well as expected. An alternative to the informational criteria can be a criterion based on Bayesian hypothesis testing, which is outlined in the article. This criterion incorporates information about the likelihood of describing prior and posterior data, the cross-accounting of which corresponds to Bayesian selection. A comparative analysis of the application of informational criteria and the new criterion, the results of which are presented in the article, supports the latter criterion, which is recommended for practical use.