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

  • Hiroshi Sakamoto


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    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 prefectureÂfs 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

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    Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa12p139.

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    Date of creation: Oct 2012
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    Handle: RePEc:wiw:wiwrsa:ersa12p139
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    1. 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.
    2. Danny Quah, 1992. "Empirical Cross-Section Dynamics in Economic Growth," FMG Discussion Papers dp154, Financial Markets Group.
    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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