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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Marina Shipitcyna
Forecasting Of Key Macroeconomic Variables With Frequentist And Bayesian Methods
Economics
(Bachelor’s programme)
2017
The aim of this work is to build forecasts for three macroindicators of the Russian economy: CPI index, indices of industrial production and bank rate using BVAR models. The peculiarity of the work is the determination of the optimal parameter and lag length: the method of maximizing marginal density was used, which was never used to predict Russian data.Also, several prior distributions are used: the Minnesota distribution, the conjugate normal-inverse Wishart prior distribution, and the Diffuse Jeffreys distribution (first applied to the construction of forecasts of Russian macro variables). As a result, the model with prior distribution of Minnesota and the fixed parameter value is optimal for all variables.

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