Abstract This summary report highlights the confluence of continued downward pressures and deflation scares in the face of looming uncertainty in China＇s key macroeconomic landscapes. Counterfactual analyses and poli...Abstract This summary report highlights the confluence of continued downward pressures and deflation scares in the face of looming uncertainty in China＇s key macroeconomic landscapes. Counterfactual analyses and policy simulations are conducted, in addition to benchmark forecasts, based on IAR-CMM model and taking into account both cyclical and secular factors. Economic deceleration is projected to continue in the short to medium term, with real GDP growth declining to 6.3% （5.5% using more reliable instead of official data） in 2016 and facing a significant risk of sliding further down in 2017. Five key factors contributing to the weak outlook, additional to frictions and impediments associated with economic transition/restructuring and lackluster domestic/external demands, are identified, including： lack of new growth/ development engine, exhaustion of government-led driving force, the crowding-out of private sectors by state-owned enterprises （SOEs） with excess capacity／capital overhang, nonperforming government sectors and officials, and twist or misinterpretation of the ＂New Normal.＂ A root cause of these problems, lying with sluggishness in China＇s transformation into a market based economy, has to do with overpowered government but underpowered market in resource allocation and government underperformance in enforcing integrity and transparency in the marketplace and in providing public goods and services. At the nexus between inclusive growth and institutional transformation are market oriented and rule of law governed structural reforms and harmonious development. As such, fundamental institutional reforms that dialectically balance demand and supply side factors and properly weigh short run stabilization against long run development should be elevated to the top of the agenda.展开更多
Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,...Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,we propose a Dynamic Conditional Feature Screening(DCFS)method tailored for high-dimensional economic forecasting tasks.Our goal is to accurately identify key variables,enhance predictive performance,and provide both theoretical foundations and practical tools for macroeconomic modeling.The DCFS method constructs a comprehensive test statistic by integrating conditional mutual information with conditional regression error differences.By introducing a dynamic weighting mechanism,DCFS adaptively balances the linear and nonlinear contributions of features during the screening process.In addition,a dynamic thresholding mechanism is designed to effectively control the false discovery rate(FDR),thereby improving the stability and reliability of the screening results.On the theoretical front,we rigorously prove that the proposed method satisfies the sure screening property and rank consistency,ensuring accurate identification of the truly important feature set in high-dimensional settings.Simulation results demonstrate that under purely linear,purely nonlinear,and mixed dependency structures,DCFS consistently outperforms classical screening methods such as SIS,CSIS,and IG-SIS in terms of true positive rate(TPR),false discovery rate(FDR),and rank correlation.These results highlight the superior accuracy,robustness,and stability of our method.Furthermore,an empirical analysis based on the U.S.FRED-MD macroeconomic dataset confirms the practical value of DCFS in real-world forecasting tasks.The experimental results show that DCFS achieves lower prediction errors(RMSE and MAE)and higher R2 values in forecasting GDP growth.The selected key variables-including the Industrial Production Index(IP),Federal Funds Rate,Consumer Price Index(CPI),and Money Supply(M2)-possess clear economic interpretability,offering reliable support for economic forecasting and policy formulation.展开更多
文摘Abstract This summary report highlights the confluence of continued downward pressures and deflation scares in the face of looming uncertainty in China＇s key macroeconomic landscapes. Counterfactual analyses and policy simulations are conducted, in addition to benchmark forecasts, based on IAR-CMM model and taking into account both cyclical and secular factors. Economic deceleration is projected to continue in the short to medium term, with real GDP growth declining to 6.3% （5.5% using more reliable instead of official data） in 2016 and facing a significant risk of sliding further down in 2017. Five key factors contributing to the weak outlook, additional to frictions and impediments associated with economic transition/restructuring and lackluster domestic/external demands, are identified, including： lack of new growth/ development engine, exhaustion of government-led driving force, the crowding-out of private sectors by state-owned enterprises （SOEs） with excess capacity／capital overhang, nonperforming government sectors and officials, and twist or misinterpretation of the ＂New Normal.＂ A root cause of these problems, lying with sluggishness in China＇s transformation into a market based economy, has to do with overpowered government but underpowered market in resource allocation and government underperformance in enforcing integrity and transparency in the marketplace and in providing public goods and services. At the nexus between inclusive growth and institutional transformation are market oriented and rule of law governed structural reforms and harmonious development. As such, fundamental institutional reforms that dialectically balance demand and supply side factors and properly weigh short run stabilization against long run development should be elevated to the top of the agenda.
文摘Current high-dimensional feature screening methods still face significant challenges in handling mixed linear and nonlinear relationships,controlling redundant information,and improving model robustness.In this study,we propose a Dynamic Conditional Feature Screening(DCFS)method tailored for high-dimensional economic forecasting tasks.Our goal is to accurately identify key variables,enhance predictive performance,and provide both theoretical foundations and practical tools for macroeconomic modeling.The DCFS method constructs a comprehensive test statistic by integrating conditional mutual information with conditional regression error differences.By introducing a dynamic weighting mechanism,DCFS adaptively balances the linear and nonlinear contributions of features during the screening process.In addition,a dynamic thresholding mechanism is designed to effectively control the false discovery rate(FDR),thereby improving the stability and reliability of the screening results.On the theoretical front,we rigorously prove that the proposed method satisfies the sure screening property and rank consistency,ensuring accurate identification of the truly important feature set in high-dimensional settings.Simulation results demonstrate that under purely linear,purely nonlinear,and mixed dependency structures,DCFS consistently outperforms classical screening methods such as SIS,CSIS,and IG-SIS in terms of true positive rate(TPR),false discovery rate(FDR),and rank correlation.These results highlight the superior accuracy,robustness,and stability of our method.Furthermore,an empirical analysis based on the U.S.FRED-MD macroeconomic dataset confirms the practical value of DCFS in real-world forecasting tasks.The experimental results show that DCFS achieves lower prediction errors(RMSE and MAE)and higher R2 values in forecasting GDP growth.The selected key variables-including the Industrial Production Index(IP),Federal Funds Rate,Consumer Price Index(CPI),and Money Supply(M2)-possess clear economic interpretability,offering reliable support for economic forecasting and policy formulation.
