| Project/Area Number |
18530186
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Applied economics
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
ISHIGAKI Tomonori Osaka Prefecture University, School of Economics, Professor (20254443)
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| Co-Investigator(Kenkyū-buntansha) |
ARAKI Nagateru Osaka Prefecture University, School of Economics, Professor (50214789)
NAKAYAMA Yuji Osaka Prefecture University, School of Economics, Associate Professor (20326284)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥1,350,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥150,000)
Fiscal Year 2007: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2006: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | Scanner Data / Microeconometrics / Bayesian Statistics |
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| Research Abstract |
We conducted microeconometric analyses using scanner data in order to empirically investigate decisions and behavior of consumers and retailers. Our project includes two sub-projects:
1. Retailers' pricing decision and competition among food brands in their stores
2. Consumers' purchase decision on music CD.
1. In the first project, we published a review article and a research note on a Bayesian hierarchical model using 88 supermarkets .scanner data in a book. In the latter, we showed that, while unstable price elasticity estimates were produced by separate ordinary least square estimation using each store data, stable estimates were obtained by a Bayesian hierarchical estimation using all stores data simultaneously. We prepare an original paper for submitting to a journal.
2. In the second project, we conducted an empirical analysis using sales data from a music CD store in Japan, which is published in refereed conference proceedings. The distinguishing feature of our data is that it contains each customer's ID number, which enables us to examine each customer's preference for music genre using his/her purchase history. We used this scanner panel data to estimate each customer's demand for music CDs. For estimation, we adopted a Bayesian hierarchical model. Utilizing estimated customers' preference, we proposed the retailer's assortment change combined with couponing both to improve its profitability and to maintain its reputation with customers.
In Both projects, we used a Markov Chain Monte Carlo (MCMC) method for estimation.
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