Forecasting and Empirical Analysis By Multivariate Time Series Models Using Bayesian Model Averaging

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K01591
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 07030:Economic statistics-related
• Research Institution: University of the Ryukyus
• Principal Investigator: Sugita Katsuhiro 琉球大学, 国際地域創造学部, 教授 (50377058)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 時系列モデル / ベイズ法 / ベイジアンモデル平均法 / 多変量モデル

Outline of Final Research Achievements

Multivariate times series models such as Vector autoregressive (VAR) model are often used for analysis and forecasting of macro economic and finantial econometrics. However, these models have many parameters to infer and contain unnecesary parameters in models. This study investigates multivariate time series models using a Bayesian model averagin method to improve forecasting performance, considering uncertainty in models and avoiding the overparameterization problem. This study consider various VAR models and nonlinear VAR models, and multivariate GARCH models using the BMA method for econometric analysis and forecasting for macro economics and financial econometrics.

Free Research Field

計量経済学

Academic Significance and Societal Importance of the Research Achievements

研究成果の学術的意義は不必要なパラメータを多く含む多変量時系列モデルに対してできるだけ不必要なパラメータを除去しモデル自身を単純化することによってより信頼性の高い計量モデルを構築し、その結果非線形多変量モデルなどによってより高い予測精度が得られることにある。