Sugita Katsuhiro

写真a

Title

Professor

Researcher Number(JSPS Kakenhi)

50377058
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Current Affiliation Organization 【 display / non-display 】

  • Duty   University of the Ryukyus   Faculty of Global and Regional Studies   economic program   Professor  

  • Concurrently   University of the Ryukyus   Graduate School of Community Engagement and Development   Professor  

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Graduate School 【 display / non-display 】

  • 　
    -
    2004.03

    University of Warwick  Graduate School, Division of Economics  Doctor's Course  Completed

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External Career 【 display / non-display 】

  • 2004.04
    -
    2007.03

    Department of Economics at Hitotsubashi University, Senir Assistant  

  • 2007.04
    -
    2009.09

    University of the Ryukyus, Assistant Professor  

  • 2009.10
    -
    2017.09

    University of the Ryukyus, Faculty of Law and Letters, Department of Comprehensive Social Systems Studies, Associate Professor  

  • 2017.10
    　
    　

     

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Research Interests 【 display / non-display 】

  • 計量経済学,ベイズ計量経済学

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Research Areas 【 display / non-display 】

  • Humanities & Social Sciences / Economic statistics

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Published Papers 【 display / non-display 】

  • Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods

    Katsuhiro Sugita

    Asian Journal of Economics and Banking ( Emerald Publishing )  6 ( 2 ) 142 - 154   2022.08 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

  • Time Series Forecasting Using a Markov Switching Vector Autoregressive Model with Stochastic Search Variable Selection Method

    Katsuhiro Sugita

    Financial Econometrics: Bayesian Analysis, Quantum Uncertainty, and Related Topics, Studies in Systems, Decision and Control 427 ( Springer )    147 - 170   2022.06 [ Peer Review Accepted ]

    Type of publication: Research paper (other science council materials etc.)

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  • Forecasting with Vector Autoregressions by Bayesian Model Averaging

    杉田　勝弘

    琉球大学経済学ワーキングペーパー ( University of the Ryukyus )  ( REWP#03 ) 1 - 13   2019.06

    Type of publication: Research paper (scientific journal)

    　View Summary

    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.

  • Forecasting with Vector Autoregressions using Bayesian Variable Selection Methods: Comparison of Direct and Iterated Methods

    杉田　勝弘

    琉球大学経済学ワーキングペーパー ( University of the Ryukyus )  No.REWP#02   1 - 18   2019.05

    Type of publication: Research paper (scientific journal)

    　View Summary

    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.

  • Evaluation of Forecasting Performance Using Bayesian Stochastic Search Variable Selection in a Vector Autoregression

    杉田　勝弘

    琉球大学経済学ワーキングペーパー ( 琉球大学国際地域創造学部経済学プログラム )  1   1 - 19   2018.08

    Type of publication: Research paper (other science council materials etc.)

    　View Summary

    This paper examines forecasting performance of a vector autoregressive (VAR) model by a Bayesian stochastic search variable selection (SSVS) method. We use several artificially generated data sets to evaluate forecasting performance using a direct multiperiod forecasting method with a recursive forecasting exercise. We find that implementing SSVS prior in a VAR improves forecasting performance over unrestricted VAR models for either non-stationary or stationary data. As an illustration of a VAR model with SSVS prior, we investigate US macroeconomic data sets with three variables using a VAR with lag length of ten, and find that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR and thus offers an appreciable improvement in forecast performance.

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