FAVAR Model for Forecasting Russian Macroeconomic Time Series

Student: Garmider Petr

Educational Programme: Economics (Bachelor)

Final Grade: 8

Year of Graduation: 2020

There are several models used for time series forecasts that focus on incorporating a huge set of information. Conventional VAR fails to employ such an approach because of the "degrees of freedom" problem. The focus of this paper is to examine factor-augmented VAR's (FAVAR's) ability to forecast the main Russian macroeconomic indicators. Results will be displayed in a table of RMSEs for different horizons using time series cross-validation procedure for each one. I discovered that FAVAR, indeed, has potential in forecasting and create particularly accurate forecasts for long-horizons. FAVAR's cross-validation error is compared with benchmark models such as ETS, ARIMA, and VAR. Latent factors in model, surely, delivers additional information, that standard VAR fails to recover.

Full text (added May 14, 2020)

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