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A Bayesian Approach for Analyzing the Dynamic Relationship Between Quarterly and Monthly Economic Indicators

  • Koki KyoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

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.

Keywords

Bayesian modeling State space model Dynamic relationship analysis Gross domestic product Whole commercial sales Analysis of japanese economy 

Notes

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.

References

  1. 1.
    Anghelache, C.: Analysis of the correlation between GDP and the final consumption. Theor. Appl. Econ. 18(9), 129–138 (2011)Google Scholar
  2. 2.
    Jiang, X.-Q., Kyo, K.: A Bayesian method for the dynamic regression analysis. Trans. Inst. Syst. Control Inf. Eng. 8(1), 8-16 (1995)CrossRefGoogle Scholar
  3. 3.
    Jiang, X.-Q., Kyo, K., Kitagawa, G.: A time-varying coefficient vector AR modeling of nonstationary covariance time series. Signal Process. 33(3), 315–331 (1993)CrossRefGoogle Scholar
  4. 4.
    Kitagawa, G.: Introduction to Time Series Modeling. CRC Press (2010)Google Scholar
  5. 5.
    Kyo, K., Noda, H.: A new algorithm for estimating the parameters in seasonal adjustment models with a cyclical component. ICIC Express Lett. Int. J. Res. Surv. 5(5), 1731–1737 (2011)Google Scholar
  6. 6.
    Kyo, K., Noda, H.: Bayesian analysis of the dynamic relationship between oil price fluctuations and industrial production performance in Japan. Inf. Int. Interdisc. J. 16(7A), 4639–4660 (2013)Google Scholar
  7. 7.
    Kyo, K., Noda, H.: Dynamic effects of oil price fluctuations on business cycle and unemployment rate in Japan. Int. J. Innov. Manage. Technol. 6(6), 374–377 (2015)Google Scholar
  8. 8.
    Liu, H., Hall, S.G.: Creating high-frequency national accounts with state-space modelling: a Monte Carlo experiment. J. Forecast. 20(6), 441–449 (2001)CrossRefGoogle Scholar
  9. 9.
    Mariano, R.S., Murasawa, Y.: A new coincident index of business cycles based on monthly and quarterly series. J. Appl. Econometrics 18(4), 427–443 (2003)CrossRefGoogle Scholar
  10. 10.
    Mariano, R.S., Murasawa, Y.: A coincident index, common factors, and monthly real GDP. Oxford Bull. Econ. Stat. 72(1), 27–46 (2010)CrossRefGoogle Scholar
  11. 11.
    Seong, B., Ahn, S.K., Zadrozny, P.: Estimation of vector error correction models with mixed-frequency data. J. Time Ser. Anal. 34(2), 94–205 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Human SciencesObihiro University of Agriculture and Veterinary MedicineInada-cho, ObihiroJapan

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