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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Anna Kuzina
Comparing Forecasting Power of Bayesian VAR with One-dimensional Time Series Models
Economics
(Bachelor’s programme)
9
2017
The main goal of this research was to compare forecast accuracy of Bayesian Vector Autoregression with time-varying parameters with forecasts of one-dimensional models and their combinations. ARIMA, exponential smoothing and Theta-method were used as one-dimensional opponents of TVP-BVAR. Two data sets were used to conduct this study. The first one consists of 9 monthly macroeconomic time series, while the second one is a set of daily price changes of 24 largest Russian companies. Forecasts for macroeconomic data set were computed for 3, 6, 12 and 24 months ahead, while for the second dataset with daily observations horizons from 7 to 260 days were used. I order to compare forecast accuracy of different models the root mean square error for each model was divided by the root mean square error of random walk model. This ratio was used as a main criterion for the comparison.

As a result of the conducted research, the following Outcomes were obtained: forecasts of TVP-BVAR were the most accurate in half cases for macroeconomic data and in more than one-third of the cases for financial time series. Nevertheless, it worth mentioning that for financial data differences in relative error was not significant which means that it is hard to tell whether it has a significant advantage over one-dimensional models.

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