琉球大学 · 国際地域創造学部

研究キーワードBayesian VAR・time series forecasting・Markov switching VAR・Bayesian model averaging・variable selection・multistep forecasting

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• Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods2022 · Katsuhiro Sugita
• Time Series Forecasting Using a Markov Switching Vector Autoregressive Model with Stochastic Search Variable Selection Method2022 · Katsuhiro Sugita
• Forecasting with Vector Autoregressions using Bayesian Variable Selection Methods: Comparison of Direct and Iterated Methods2019 · This paper compares multi-period forecasting performances by direct and iterated method using a Bayesian vector autoregressions with the stochastic search variable selection (SSVS) priors. The forecasting performances are evaluated using the artificially generated data with both nonstationary and stationary process. In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data. As an illustration, US macroeconomic data sets with three variables are examined to compare iterated and direct forecasts using the unrestricted VAR model and the SSVS VAR model. Overall, iterated forecasts using model with the SSVS generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.
• Forecasting with Vector Autoregressions by Bayesian Model Averaging2019 · This paper examines how vector autoregression model by Bayesian model averaging method can improve forecasting performance. Bayesian model averaging selects significant variables in vector autoregression model that contains many insignificant variables, and thus alleviates over-parameterization problem. For empirical application, macroeconomic data for three countries - US, UK and Japan - are examined. I find that the Bayesian model averaging method can improve forecasting performance.

• アクチュアリーとファイナンスにおけるベイジアン・モデリング2024 · 基盤研究(A)
• ベイジアンモデル平均法を使った多変量時系列モデルによる予測と実証分析2020 · 基盤研究(C)
• ベイジアンSSVS法を使った非線形多変量時系列モデルによる予測と実証分析2017 · 基盤研究(C)
• ベイズ法による非観測データを含む多変量時系列モデルの分析とマクロ経済分析への応用2009 · 基盤研究(C)

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