城镇居民消费过度敏感性的统计分析

本文在文献综述的基础上,指出了现有研究中的不足,即缺少不同收入阶层的消费过度敏感性的分析以及由于样本容量的不足导致模型拟合及预测能力不强。本文主要利用panel data模型分析了我国城镇不同收入阶层居民消费的过度敏感性,验证出各阶层都存在着过度敏感性,且过度敏感性的大小在各阶层中呈现倒"草帽"型分布。     

序列相关的微观结构噪声估计

基于股票有效价格计算的已实现波动率(Realized Variance)可作为股票收益波动率的估计,且在一定条件下,这一估计是无偏的和一致的。然而实际观测到的价格由于受到市场微观结构导致的噪声的干扰,与有效价格并不一致。因此,在高频数据环境下必须考虑如何降低噪声干扰。本文基于Hansen和Lunde给出的在噪声序列存在相关性假设下的一种关于RV的无偏估计,进一步推导出在此情形下估计噪声方差的方法。我们的估计挖掘了不同频率下的股票交易高频数据所反映出的信息,利用传统的在噪声影响下的有偏RV估计与Hansen和Lunde的无偏RV估计之间的差估计噪声。同时,本文也给出了在实践中如何确定这些频率的方法。     

The impacts of risk-taking behaviour and competition on technical efficiency: Evidence from the Chinese banking industry

This paper tests the impact of risk and competition on efficiency in the Chinese banking industry over the period 2003–2013. Comprehensive types of risk-taking behaviour are considered including credit risk, liquidity risk, capital risk, and insolvency risk. Competition is measured by the Lerner index. The results are cross-checked using an alternative econometric technique as well as an alternative competition indicator. The findings show that the technical and pure technical efficiencies of Chinese commercial banks are significantly and negatively affected by liquidity risk. They further show that greater competition precedes declines in technical and pure technical efficiencies of Chinese commercial banks. The results suggest that Chinese bank efficiency is significantly affected by bank diversification, banking sector development, stock market development, inflation and GDP growth rate. The findings also indicate that, compared to state-owned commercial banks, joint-stock commercial banks and city commercial banks have lower technical and pure technical efficiencies.     

我国居民杠杆率现状及影响因素研究

近年来,我国居民部门杠杆率的快速上升引起社会各界关注。本文使用货币信贷和城镇储户调查数据,对我国居民部门杠杆率和偿债能力现状进行了分析,并运用各省住户贷款数据计算各省的居民杠杆率,使用面板数据模型对居民杠杆率上升的原因进行了实证分析。结果表明,房价的快速上涨和住房销售的增长都对居民部门杠杆率的上升有显著正向影响,其中房价上涨的影响程度更大。此外,金融发展水平和老年人抚养比对居民杠杆率有正向影响,少年人抚养比对居民杠杆率有负向影响。     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

An adaptive approach to forecasting three key macroeconomic variables for transitional China

The macroeconomic forecasts for emerging economies often suffer from the constraints of instability and limited data. In light of these constraints, we propose the use of a local autoregressive (LAR) model with a data-driven estimation window, i.e., a local homogenous interval, that is adaptively identified to strike a balance between information efficiency and stability. When applied to three key macroeconomic variables of China, the LAR model substantially outperforms the alternative models for various forecast horizons of 3 to 12 months, with forecast error reductions of between 4% and 64% for the IP growth, and between 1% and 68% for the inflation rate. The one-quarter ahead performance of the LAR model matches that of a well-known survey forecast. The patterns of the identified local intervals also coincide with the characteristic evolution of the gradual reforms and monetary policy shifts in China. In short, the LAR model is suitable for not only forecasting, but also the real-time monitoring of the effects of regime and policy changes in emerging economies.     

基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。     

大数据时代进行服务创新的思考

在大数据时代,服务创新必须与超级计算技术密切结合,同时要注意一些应该充分体现的原则。可以开展服务创新的领域是极其广泛的,在经济管理领域,主要可以在企业经营决策、金融理财、商业模式、选择业务外包等诸多领域提供创新的服务。