Suchergebnisse

With strong economic growth, the auto industry has made great breakthroughs in recent years and has become a backbone industry in China, while cars play an increasingly important role, and are now the principal part of the auto industry. Both China's government and academic circles take strong interest in the prediction of CPR (i.e. car penetration rate or cars per thousand people), which will be the main guidance for the future industry policy. We summarize the existing problems in recent research and propose to use nonparametric methods to estimate the CPR and its elasticity with respect to GDP per capita (GDPPC). The results indicate that the nonparametric methods provide a much better fit than the conventional OLS method, and more importantly, it captures the nonlinearity of the elasticity of CPR with respect to GDPPC. Finally, we predict future CPR in China.

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon andWeidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to zero. Further, we demonstrate that our shrinkage estimators of the nonzero slope parameters exhibit some oracle property. We conduct Monte Carlo simulations to demonstrate the superb finite-sample performance of the proposed method. We apply our method to study the determinants of economic growth and find that in addition to three common unobserved factors selected by our method, government consumption share has negative effects, whereas investment share and lagged economic growth have positive effects on economic growth.

The dramatic rise of house prices in many cities of China has brought huge attention from both the governmental and academic circles. There is a huge debate on whether the increasing house prices are driven by market fundamentals or just by speculation. Like Levin and Wright (1997a, 1997b), we decompose house prices in China into fundamental and non−fundamental components. We also consider potential nonlinear feedback from the historical growth rate of house prices on the current house prices and propose a semiparametric approach to estimate the speculative components in the model. We demonstrate that the non−fundamental part contributes a relatively small proportion of the rise of house prices in China.