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The aim is to forecast China's CPI inflation and urban registered unemployment rate using econometric and machine learning models.

An Empirical Study of Inflation and Unemployment Forecasting in China

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Randomized trial forecasts unemployment and inflation in China, suggesting improved accuracy with machine learning.

The aim is to forecast China's CPI inflation and urban registered unemployment rate using econometric and machine learning models.
Used Ordinary Least Squares (OLS) as a linear benchmark and Extreme Gradient Boosting (XGBoost) for nonlinear forecasting.
Analyzed annual macroeconomic and monetary data for forecasting.
Conducted comparative evaluation of in-sample and out-of-sample forecasting performance.
XGBoost shows improved predictive accuracy for unemployment compared to OLS.
OLS provides a clearer economic interpretation despite lower predictive accuracy.
Integration of econometric and machine learning methods enhances macroeconomic forecasting in China.

Ting Yangxian (2026) studied this question.

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