Measuring the Macroeconomic Uncertainty Based on the News Text by Supervised LDA for Investor’s Decision Making

Yono, Kyoto; Izumi, Kiyoshi; Sakaji, Hiroki; Shimada, Takashi; Matsushima, Hiroyasu

doi:10.1007/978-3-030-38227-8_15

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2022

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Cited by 2 publications

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“…In terms of variable selection, the usual empirical variables are often used: export value variables, real exchange rate variables, GDP variables of importing and exporting countries, and their lagged variables. In addition, since macrovariables tend to be highly seasonally cyclical, they are generally seasonally adjusted in the literature [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

China’s Economic Forecast Based on Machine Learning and Quantitative Easing

Qiu

2022

Computational Intelligence and Neuroscience

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In this paper, six variables, including export value, real exchange rate, Chinese GDP, and US IPI, and their seasonal variables, are used as determinants to model and forecast China’s export value to the US using three methods: BP neural network, ARIMA, and AR-GARCH. Error indicators were chosen to compare the simulated and predicted results of the three models with the real values. It is found that the results of all three models are satisfactory, although there are some differences in their simulation and forecasting capabilities, but the ARIMA model has a clear advantage. This paper analyses the reasons for these results and proposes suggestions for improving China’s exports in the context of the models.

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Section: Related Workmentioning

confidence: 99%