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

Hiroshi Sakamoto ()

ERSA conference papers from European Regional Science Association

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

Date: 2012-10
New Economics Papers: this item is included in nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:wiw:wiwrsa:ersa12p139

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