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Prediction of the Prefectural Economy in Japan Using a Stochastic Model

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  • Sakamoto, Hiroshi

Abstract

This study develops a simple forecasting model using Japanese prefectural data. The Markov chain, known as a stochastic model, corresponds to a first-order vector auto-regressive (VAR) model. If the transition probability matrix can be appropriately estimated, a forecasting model using the Markov chain can be constructed. This study introduces a methodology for estimating the transition probability matrix of the Markov chain using least-squares optimization. The model is used first to analyze economy-wide changes encompassing all Japanese prefectures up to 2020. Second, a shock emanating from one prefecture is inserted into the transition probability matrix to investigate its influence on the other prefectures. Finally, a Monte Carlo experiment is conducted to refine the model's predicted outcomes. Although this study's model is simple, we provide more sophisticated forecasting information for prefectural economies in Japan.

Suggested Citation

  • Sakamoto, Hiroshi, 2013. "Prediction of the Prefectural Economy in Japan Using a Stochastic Model," AGI Working Paper Series 2013-02, Asian Growth Research Institute.
    • Handle: RePEc:agi:wpaper:00000069
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    References listed on IDEAS

    as
    1. Quah, Danny T, 1996. "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," Economic Journal, Royal Economic Society, vol. 106(437), pages 1045-1055, July.
    2. Quah, Danny, 1993. "Empirical cross-section dynamics in economic growth," European Economic Review, Elsevier, vol. 37(2-3), pages 426-434, April.
    3. Quah, Danny, 1996. "Twin peaks : growth and convergence in models of distribution dynamics," LSE Research Online Documents on Economics 2278, London School of Economics and Political Science, LSE Library.
    4. 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.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. Danny Quah, 1996. "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," CEP Discussion Papers dp0280, Centre for Economic Performance, LSE.
    7. Michael Sonis & Geoffrey J. D. Hewings (ed.), 2009. "Tool Kits in Regional Science," Advances in Spatial Science, Springer, number 978-3-642-00627-2, February.
    8. Michael Sonis & Dimitrios S. Dendrinos, 2009. "Socio-Spatial Dynamics and Discrete Non-Linear Probabilistic Chains," Advances in Spatial Science, in: Michael Sonis & Geoffrey J. D. Hewings (ed.), Tool Kits in Regional Science, chapter 7, pages 177-197, Springer.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Prefectural economy; Japan; Stochastic model; Markov chain;
    All these keywords.

    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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