Abstract
We propose an approach for analyzing the dynamic relationship between a quarterly economic indicator and a monthly economic indicator. In this study, we use Japan’s real gross domestic product (GDP) and whole commercial sales (WCS) as examples of quarterly and monthly indicators, respectively. We first estimate stationary components from the original time series for these indicators, with the goal of analyzing the dynamic dependence of the stationary component of GDP on that of WCS. To do so, we construct a set of Bayesian regression models for the stationary component of GDP based on the stationary component of WCS, introducing a lag parameter and a time-varying coefficient. To demonstrate this analytical approach, we analyze the relationship between GDP and WCS-FAP, the WCS of farm and aquatic products, in Japan for the period from 1982 to 2005.
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Acknowledgment
This work is supported in part by a Grant-in-Aid for Scientific Research (C) (16K03591) from the Japan Society for the Promotion of Science. I thank Deborah Soule, DBA, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
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Kyo, K. (2019). A Bayesian Approach for Analyzing the Dynamic Relationship Between Quarterly and Monthly Economic Indicators. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_2
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DOI: https://doi.org/10.1007/978-3-030-01174-1_2
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