The Application of Bayesian Estimation for the Prediction of Economic Trends

Norimasa Nakamura and Eisuke Kita ()
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Norimasa Nakamura: Nagoya University
Eisuke Kita: Nagoya University

The Review of Socionetwork Strategies, 2022, vol. 16, issue 2, 239-258

Abstract: Abstract Since economic trends have a great influence on corporate activities, predicting whether the economy is in an expansion period or in retreat is important. Business condition indexes used in Japan that quantify the economy include the diffusion index (DI) and the composite index (CI). A method for predicting economic judgement is presented in this study. An economic trend is taken as an objective function and the DI and CI values are explanatory variables. The prediction model is defined as a Bayesian network. In Bayesian networks, random variables are used as nodes, and the dependency between variables is represented by a directed graph. Japan’s economic trends and DI and CI values from 1985 to 2020 are taken as experimental data. The forecast model is determined using the data from 1985 to 2017 as learning data, and the economic trend from 2018 to 2020 is predicted. The proposed algorithm is compared with a linear model for time-series data. The proposed algorithm shows better accuracy than the linear models.

Keywords: Diffusion index (DI); Composite index (CI); Bayesian network; Linear model; Prediction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12626-022-00114-y

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