Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/120477
Authors: 
Sakamoto, Hiroshi
Year of Publication: 
2012
Series/Report no.: 
52nd Congress of the European Regional Science Association: "Regions in Motion - Breaking the Path", 21-25 August 2012, Bratislava, Slovakia
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fs 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.
Subjects: 
Prefectural economy
Japan
Stochastic model
Markov chain
JEL: 
C15
C53
C61
O53
R12
Document Type: 
Conference Paper

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