Abstract
Forecasting the business cycle can help policymakers implement economic policies more effectively. This paper selects 62 macroeconomic and financial indicators and divides them into two data sets to forecast China's business cycle. The data of the past 36 months is used to predict China’s business cycle for the next month by the simple rolling window method. For testing the training set and determining model parameters, five machine learning models are used: XGBoost, SVM, Logistic Regression, Decision Tree, and Random Forest. The statistical evaluation indicators of the confusion matrix show that these five machine learning algorithms can reliably anticipate China's economy cycle, with Logistic Regression outperforming the others. At the same time, the paper compares the model predictions with the actual values and discusses the differences between them.
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The authors gratefully acknowledge financial support from the National Social Science Fund of China (No. 19CJL028).
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Appendices
Appendix A: Statistical Description of the Data
The overview of data 1 and data 2 are given in Table
7 and
8. The trend diagrams of the dependent and explanatory variables of data 1, containing the relevant indices of Y, RPI, demand deposit rate, and CPI etc. is shown in Fig.
Trend of the dependent and explanatory variables
5.
Appendix B: Prediction Results of Small Data Set
To obtain a more accurate ranking of factor importance for machine learning models, we employ the interpretable machine learning method-Shap method. SHAP (Shaply Additive Explanation) ranks the degree of influence of features by the marginal contribution of each feature in a computerized machine learning model based on the contribution allocation method of cooperative games; it is a post-hoc interpretable method that can substantially improve the interpretability of machine learning models. The TreeSHAP utilized in this paper was proposed by Lundberg and Lee (2017) and can be used to explain models including logistic regression, random forest, and XGBoost. Because logistic regression has the maximum accuracy for data 1, we rank the logistic regression features. Figure
Feature importance ranking based on shap method
6 depicts the top ten ranked features. The top ten characteristics include money (M1) supply up (%) month-on-month, cumulative growth in investment in new fixed assets in real estate development (%), Corporate Goods Transaction Price Index (CGPI) up (%), Production Price Index (PPI) up (%) for the previous n months of data. Therefore, we use these four variables to forecast the economic cycle, and Table
9 and Fig.
ROC curves for each machine learning model using small data set
7 illustrate the accuracy of our forecast. By removing redundant variables, it is evident that the four variables can also achieve high prediction accuracy, with Logistic Regression model having the maximum accuracy of 80% and SUV having the second highest accuracy of 77%.
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Tang, P., Zhang, Y. China's business cycle forecasting: a machine learning approach. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10549-w
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DOI: https://doi.org/10.1007/s10614-024-10549-w