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Future Prediction of the Prefectural Economy in Japan: Using a Stochastic Model


  • Hiroshi Sakamoto



This study develops an easy forecasting model using prefectural data in Japan. The Markov chain known as a stochastic model corresponds to the vector auto-regressive (VAR) model of the first order. If the transition probability matrix can be appropriately estimated, the forecasting model using the Markov chain can be constructed. Therefore, this study introduces the methodology to estimate the transition probability matrix of the Markov chain using the least-squares optimization. For application, firstly change of the all-prefectures economy by 2020 is analyzed using this model. Secondly, in order to investigate the influence to other prefecture, a specific prefecture's shock is put into a transition probability matrix. Lastly, in order to take out the width of prediction, the Monte Carlo experiment is conducted. Despite this model is very simple, we provide the more sophisticated forecasting information of the prefectural economy in Japan through the complicated extension. JEL classification: C15, C53, C61, O53, R12 Keywords: Prefectural economy, Japan, Stochastic model, Markov chain

Suggested Citation

  • Hiroshi Sakamoto, 2012. "Future Prediction of the Prefectural Economy in Japan: Using a Stochastic Model," ERSA conference papers ersa12p139, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa12p139

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    References listed on IDEAS

    1. Quah, Danny, 1993. "Empirical cross-section dynamics in economic growth," European Economic Review, Elsevier, vol. 37(2-3), pages 426-434, April.
    2. Sakamoto, Hiroshi & Islam, Nazrul, 2008. "Convergence across Chinese provinces: An analysis using Markov transition matrix," China Economic Review, Elsevier, vol. 19(1), pages 66-79, March.
    3. Quah, Danny T., 1996. "Empirics for economic growth and convergence," European Economic Review, Elsevier, vol. 40(6), pages 1353-1375, June.
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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