Forecasting time series of economic processes by model averaging across data frames of various lengths

ABSTRACT

This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.

KEYWORDS:

• Data frame selection
• Bayesian information criterion
• Akaike information criterion
• forecast combination
• model averaging
• interval forecast
• mean-squared forecast error model averaging

JEL Codes:

• C52
• C53

Acknowledgements

This research was carried out within the framework of the basic part of a state commission in the sphere of scientific activity of the Ministry of Education and Science of the Russian Federation on the topic ‘Intellectual analysis of large-scale text data in finance, business and education on the basis of adaptive semantic models’, project number 9577.

Disclosure statement

The author declares that he has no conflict of interests.

ORCID

Nikita A. Moiseev http://orcid.org/0000-0002-5632-0404

Additional information

Funding

This work was supported by Ministry of Education and Science of the Russian Federation [grant number 9577].