40 research outputs found

    Inequality and Poverty in China during Reform

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    This paper provides an overview of the evolution of income inequality and poverty in China from 1987 to 2002, documenting significant increases of inequality within China's urban and rural populations. In rural areas, increased inequality is primarily related to the disequalizing role of non-agricultural self-employment income and the slow growth in agricultural income from the mid-1990s onward. Poverty persists, and tied in part to slow growth in agricultural commodity prices. In urban areas, the declining role of subsidies and entitlements, the increase in wage inequality, and the layoffs during restructuring have fueled the growth in inequality within urban areas. Poverty levels, however, are very low. China should give more emphasis on education, training, and other human development efforts in its poverty reduction strategy since return to education increased rapidly and became a major source of inequality. A nationwide "social safety net" and an effective redistributive taxation system should be adopted and implemented to ensure that the poor can benefit from the fruits of rapid economic growth.Income inequality, poverty, welfare, growth, reform, transition, policy, China

    Fast Bivariate P-splines: the Sandwich Smoother

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    We propose a fast penalized spline method for bivariate smoothing. Univariate Pspline smoothers Eilers and Marx (1996) are applied simultaneously along both coordinates. The new smoother has a sandwich form which suggested the name “sandwich smoother” to a referee. The sandwich smoother has a tensor product structure that simplifies an asymptotic analysis and it can be fast computed. We derive a local central limit theorem for the sandwich smoother, with simple expressions for the asymptotic bias and variance, by showing that the sandwich smoother is asymptotically equivalent to a bivariate kernel regression estimator with a product kernel. As far as we are aware, this is the first central limit theorem for a bivariate spline estimator of any type. Our simulation study shows that the sandwich smoother is orders of magnitude faster to compute than other bivariate spline smoothers, even when the latter are computed using a fast GLAM (Generalized Linear Array Model) algorithm, and comparable to them in terms of mean squared integrated errors. We extend the sandwich smoother to array data of higher dimensions, where a GLAM algorithm improves the computational speed of the sandwich smoother. One important application of the sandwich smoother is to estimate covariance functions in functional data analysis. In this application, our numerical results show that the sandwich smoother is orders of magnitude faster than local linear regression. The speed of the sandwich formula is important because functional data sets are becoming quite large

    Does Index Futures Trading Reduce Volatility in the Chinese Stock Market? A Panel Data Evaluation Approach

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    This paper investigates the effect of introducing index futures trading on the spot price volatility in the Chinese stock market. We employ a recently developed panel data policy evaluation approach (Hsiao et al. 2011) to construct counterfactuals of the spot market volatility, based mainly on cross-sectional correlations between the Chinese and international stock markets. This new method does not need to specify a particular regression or a time series model for the volatility process around the introduction date of index futures trading, and thus avoids the potential omitted variable bias caused by uncontrolled market factors in the existing literature. Our results provide empirical evidence that the introduction of index futures trading significantly reduces the volatility of the Chinese stock market, which is robust to different model selection criteria and various prediction approaches.

    Solving Euler equations via two-stage nonparametric penalized splines

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    动态随机一般均衡(DSGE)和资本资产定价模型是近年来在宏观经济学和宏观金融市场研究中广泛采用的模型方法。DSGE模型要求对未知政策函数(比如价格红利比率等)进行计算求解,从而得到模型结论和政策建言。然而,现有的经济、金融文献大多在金融数据服从某些特定概率分布的假设下,依赖数值方法来对DSGE模型做近似求解,而这会带来模型误设和近似误差问题。这种模型误设和近似误差可能会严重影响到求解动态均衡结果的有效性和可信度。该文首次提出运用非参数估计方法对DSGE模型中的政策函数进行求解,从而替代传统的近似求解法,这克服了过去文献存在的模型误设和近似误差的问题。该文是洪永淼教授牵头,以厦门大学为依托单位,联合中国科学院数学与系统科学研究院共同立项的“计量建模与经济政策研究”国家自然科学基础科学中心项目的阶段性成果。【Abstract】This study proposes a novel estimation-based approach to solving asset pricing models for both stationary and time-varying observations. Our method is robust to misspecification errors while inheriting a closed-form solution. By representing the Euler equation into a well-posed integral equation of the second kind, we propose a penalized twostage nonparametric estimation method and establish its optimal convergence under mild conditions. With the merit of penalized splines, our estimate is less sensitive to the spline setting and we also design a fast data-driven algorithm to effectively tune the key smoother, i.e. the penalty amount. Our approach exhibits excellent finite sample performance. Using the US data from 1947 to 2017, we reinvestigate the return predictability and find that the estimated implied dividend yield significantly predicts lower future cash flows and higher interest rates at short horizons.Hong's research is supported by National Science Foundation of China (NSFC) (No. 71988101), which is the Basic Scientific Center Project entitled as Econometric Modelling and Economic Policy Studies. Cui’s research is supported by the Research Grants Council of Hong Kong, China (No. 11500119 and 21504818) and NSFC (No. 71803166). Li’s research is supported by NSFC (No. 71571154 and No. 71631004)
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