IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa12p139.html

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

Author

Listed:
  • Hiroshi Sakamoto

Abstract

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
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa12/e120821aFinal00141.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    4. Michael Sonis & Geoffrey J. D. Hewings (ed.), 2009. "Tool Kits in Regional Science," Advances in Spatial Science, Springer, number 978-3-642-00627-2.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hiroshi Sakamoto, 2013. "Prediction Of The Prefectural Economy In Japan Using A Stochastic Model," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 13-24, June.
    2. María Hierro & Adolfo Maza, 2015. "From Discrete To Continuous-Time Transition Matrices In Intra-Distribution Dynamics Analysis: An Application To Per Capita Wealth In Europe," Bulletin of Economic Research, Wiley Blackwell, vol. 67(3), pages 227-235, July.
    3. Cheong, Tsun Se & Wu, Yanrui, 2018. "Convergence and transitional dynamics of China's industrial output: A county-level study using a new framework of distribution dynamics analysis," China Economic Review, Elsevier, vol. 48(C), pages 125-138.
    4. Cheong, Tsun Se & Wu, Yanrui, 2013. "Regional disparity, transitional dynamics and convergence in China," Journal of Asian Economics, Elsevier, vol. 29(C), pages 1-14.
    5. Sakamoto, Hiroshi, 2013. "Prediction of the Prefectural Economy in Japan Using a Stochastic Model," AGI Working Paper Series 2013-02, Asian Growth Research Institute.
    6. M. Herrerías, 2012. "Weighted convergence and regional growth in China: an alternative approach (1952–2008)," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(3), pages 685-718, December.
    7. Hiroshi Sakamoto & Jin Fan, 2013. "Regional Income Disparity In China Using Value-Added Data: Decomposition And Distribution Dynamics," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 25(1), pages 16-33, March.
    8. Jian-Xin Wu & Ling-Yun He, 2017. "The Distribution Dynamics of Carbon Dioxide Emissions Intensity across Chinese Provinces: A Weighted Approach," Sustainability, MDPI, vol. 9(1), pages 1-19, January.
    9. Michael Beenstock & Daniel Felsenstein, 2003. "Decomposing the Dynamics of Regional Earnings Disparities in Israel," ERSA conference papers ersa03p90, European Regional Science Association.
    10. Catherine Fuss, 1999. "Mesures et tests de convergence : une revue de la littérature," Revue de l'OFCE, Programme National Persée, vol. 69(1), pages 221-249.
    11. Maria Jesus Herrerias & Javier Ordóñez, 2014. "Stochastic Regional Convergence in China: The Role of Regional Clusters in a Nonlinear Perspective (1952–2007)," Pacific Economic Review, Wiley Blackwell, vol. 19(2), pages 153-169, May.
    12. Leone Leonida & Leone Leonida & Daniel Montolio, 2003. "Public Capital, Growth and Convergence in Spain. A Counterfactual Density Estimation Approach," Working Papers 2003/3, Institut d'Economia de Barcelona (IEB).
    13. Danny Quah, 1996. "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," CEP Discussion Papers dp0280, Centre for Economic Performance, LSE.
    14. Hiranya Nath & Kiril Tochkov, 2013. "Relative inflation dynamics in the new EU member countries of Central and Eastern Europe," Empirical Economics, Springer, vol. 45(1), pages 1-22, August.
    15. Andrés Maroto Sánchez & Juan Ramón Cuadrado Roura, 2008. "New Regional convergence in productivity and productive structure. Application to European Southern countries," Working Papers 11/08, Instituto Universitario de Análisis Económico y Social.
    16. Boris Branisa & Adriana Cardozo, 2009. "Regional Growth Convergence in Colombia Using Social Indicators," Ibero America Institute for Econ. Research (IAI) Discussion Papers 195, Ibero-America Institute for Economic Research.
    17. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    18. Breandán Ó'hUallacháin, 2008. "Regional growth transition clubs in the United States," Papers in Regional Science, Wiley Blackwell, vol. 87(1), pages 33-53, March.
    19. Licia Ferranna & Margherita Gerolimetto & Stefano Magrini, 2016. "The effect of immigration on convergence dynamics in the US," Working Papers 2016:27, Department of Economics, University of Venice "Ca' Foscari".
    20. Roberto Ezcurra & Pedro Pascual, 2006. "Factor decomposition of spatial disparities: The case of the European regions," Economics Bulletin, AccessEcon, vol. 18(6), pages 1-6.

    More about this item

    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)

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa12p139. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.