Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach

Tutberidze, Davit and Japaridze, Dimitri (2017) Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach. Bulletin of Taras Shevchenko National University of Kyiv. Economics., 191 (2). pp. 42-49. ISSN 1728-3817 (general), 1728-2667 (print version), 2079-908X (electronic version)

Tutberidze, Japaridze - Article_2017.pdf - Published Version

Download (3MB) | Preview


There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR) modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed – a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

Item Type: Article
Uncontrolled Keywords: Forecasting, Macroeconomic Modeling, Bayesian VAR, Litterman Prior, Scenario Analysis, IFRS 9
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Divisions: Faculties/Schools > School of Business
Depositing User: დავით თუთბერიძე
Date Deposited: 11 May 2017 13:26
Last Modified: 11 May 2017 13:26

Actions (login required)

View Item View Item