Abstract
Regional cities in Japan have a lot of social issues. Various measures are being considered to solve these social issues, but it is difficult to ascertain and implement practical and effective measures. In this study, we proposed a new causal inference for selecting indicators that have causal relations with the social issues. If there was a causal relation between two sets of time series data, the slope of the approximation line of the time-shifted correlation coefficients at the base time returned a negative value. The causal inference was verified by using samples of time series data. In addition, we achieved future predictions by the vector autoregressive model using the causal indicators. The model was verified using the actual time series data of 87 regional cities. As a result, it was possible to simulate future predictions and to calculate the effects by introducing practical and effective measures to solve social issues.
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References
Rubin, D.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66(5), 688–701 (1974)
Pearl, J.: Bayesian networks: a model of self-activated memory for evidential reasoning. In: Proceedings, Cognitive Science Society, pp. 329–334 (1985)
Shimizu, S., Hyvärinen, A., Kano, Y., Hoyer, P.O.: Discovery of non-Gaussian linear causal models using ICA. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pp. 526–533 (2005)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S.: Detecting causality in complex ecosystems. Science 338, 496–500 (2012)
Ministry of Internal Affairs and Communications: e-Stat: portal site of official statistics of Japan (2016). http://www.e-sat.go.jp/SG1/estat/eStatTopPortalE.do
Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. Am. Stat. 42(1), 59–66 (1988)
Rderrick, T., Tbates, B., Sdufek, J.: Evaluation of time-series data sets using the Pearson product-moment correlation coefficient. Med. Sci. Sports Exerc. 26(7), 919–928 (1994)
Mrobinson, P.: Correlation testing in time series, spatial and cross-sectional data. J. Econometrics 147(1), 5–16 (2008)
Sims, C.A.: Macroeconomics and reality. Econometrica 48, 1–48 (1980)
National Institute of Population and Social Security Research: Regional population projections for Japan: 2010–2040, Population Research Series, p. 330 (2013)
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Nakazawa, K., Shiota, T., Tanaka, T. (2018). Future Prediction of Regional City Using Causal Inference Based on Time Series Data. In: Obaidat, M., Ören, T., Merkuryev, Y. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2016. Advances in Intelligent Systems and Computing, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-69832-8_16
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DOI: https://doi.org/10.1007/978-3-319-69832-8_16
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