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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Artem Kondrashov
Use of BVAR Models for Forecasting Russian Macroeconomic Indicators
2018
Contemporary economy is a complex subject, and accurate forecasting is important for conducting macroeconomic policies, as the effects of the latter are often lagged.

Lately, many researchers, including employees of central banks find that Bayesian VAR models are generally more accurate than conventional approaches.

Moreover, BVARs are capable to account for more data, solving the problem of overparametrisation.

In this paper I adopt Bayesian Compressed Vector Autoregression (BCVAR) model (Koop, Korobilis, Petenuzzo (2018)) with conjugate Normal-Inverse Wishart prior modified with "sum-of-coefficients" and "initial observation" (Sims, Zha, (1998)) and discuss it capabilities in terms of forecasting Russian macroeconomic indicators.

The data consists of 30 monthly time series running from January 1999 to December 2015.

The forecasts are compared against RWWN and VAR models.

The obtained results suggest that the BCVAR model generally beats the benchmark model, with some variables showing extremely good forecast quality.

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