Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

Nonlinear IV unit root tests in panels with cross-sectional dependency

We propose a unit root test for panels with cross-sectional dependency. We allow general dependency structure among the innovations that generate data for each of the cross-sectional units. Each unit may have different sample size, and therefore unbalanced panels are also permitted in our framework. Yet, the test is asymptotically normal, and does not require any tabulation of the critical values. Our test is based on nonlinear IV estimation of the usual augmented Dickey–Fuller type regression for each cross-sectional unit, using as instruments nonlinear transformations of the lagged levels. The actual test statistic is simply defined as a standardized sum of individual IV t-ratios. We show in the paper that such a standardized sum of individual IV t-ratios has limit normal distribution as long as the panels have large individual time series observations and are asymptotically balanced in a very weak sense. We may have the number of cross-sectional units arbitrarily small or large. In particular, the usual sequential asymptotics, upon which most of the available asymptotic theories for panel unit root models heavily rely, are not required. Finite sample performance of our test is examined via a set of simulations, and compared with those of other commonly used panel unit root tests. Our test generally performs better than the existing tests in terms of both finite sample sizes and powers. We apply our nonlinear IV method to test for the purchasing power parity hypothesis in panels.     

中国通货膨胀率持久性变化研究及政策含义分析

通胀率持久性在通胀率动态研究中备受学界的关注,并且直接影响现代货币政策传导机制的终解方程式。本文对我国通胀率持久性的统计特性做了严谨的计量检验和分析,应用“格点拔靴(自举)”中值无偏估计和Exp-Wald未知断点检验来捕捉我国物价波动持久性的特征。统计结果显示,通胀率持久性在高通胀时期走高,而在物价波动减小的20世纪90年代中后期显著减弱。我们讨论了这一发现对相关货币政策分析机制的含义。     

Do fiscal rules cause better fiscal balances? A new instrumental variable strategy

This paper estimates the causal effect of fiscal rules on fiscal balances in a panel of 142 countries over the period 1985–2015. Our instrumental variable strategy exploits the geographical diffusion of fiscal rules across countries. The intuition is that reforms in neighboring countries may affect the adoption of domestic reforms through peer pressure and imitational effects. We find that the mere existence of fiscal rules correlates with lower deficits, but the positive link disappears when endogeneity is correctly addressed. However, when considering the strength of rules through a continuous index of fiscal rules’ design, we show that well-designed rules have a statistically significant impact on fiscal balances. We conduct several robustness tests and show that our results are generally robust and not affected by weak instrument problems.     

数字经济、城市创新与区域收敛

当前,数字经济蓬勃发展,数据已成为除劳动、土地和资本等传统生产要素之外的新要素,能否依托数字经济发展赋能城市创新？文章在对数字经济的概念和内涵进行基本界定后,借助微观大数据构造的"互联网+数字经济"指数,实证分析了2015-2018年中国283个地区数字经济发展对城市创新及创新差距的影响。研究结果显示,数字经济发展能够显著促进城市创新水平提升。其中,人力资本集聚效应和创业活力增强效应是数字经济赋能城市创新的两条重要路径。在替换被解释变量、消除样本选择偏差、选择分位数回归,以及创新性地采用各地级市市长过去修习的专业作为历史工具变量检验后,这一结论依然稳健成立。进一步空间杜宾效应分析表明,数字经济发展具有显著的空间溢出效应特征,不仅促进了本地区创新水平提升,而且对相邻地区的创新产出也产生了实质性影响,有利于缩小区域创新差距,实现区域创新收敛。此外,相较于实用新型专利和外观设计专利,数字经济发展对城市发明专利的影响更显著,说明数字经济发展推动的创新是真正意义上的质量创新而非策略创新。文章为评估数字经济发展的影响效果提供了理论依据和经验支撑,也为理性看待数字经济、防止出现数字鸿沟,实现区域创新均衡发展提供了有益启示。     

基于Hill估计的VaR计算——以次贷危机为例

对极值分布的尾部形式、Hill估计量、风险价值分析等进行了理论分析,应用极值理论中的Hill估计对2002-01-04至2008-09-18期间的上证综指、道琼斯指数、恒生指数等做了风险价值的计算,比较和分析各国面临的市场风险的大小：认为美国次贷危机的爆发给全球金融市场带来了巨大风险,对中国的影响尤为巨大。     

The inflation rates may accelerate after all: panel evidence from 19 OECD economies

The stationarity of inflation has many important economic implications. Most panel-based empirical studies do not handle cross-sectional dependence, which will result in power distortion. This paper applies a nonlinear IV estimator to calculate the test statistic of panel unit root (Chang in J Econom 110:261–292, 2002), which accounts for general cross-sectional correlation. Using monthly inflation rates, two statistics proposed by Im et al. (J Econom 115:53–74, 2003) reject the unit root; however, the nonlinear IV statistic accepts the unit root. That is, the ignored cross-sectional correlation may lead to over-rejection of the unit root null. In a nutshell, unlike current literature, the inflation rates may accelerate after all.     

中国工业企业自主创新的影响因素和产出绩效研究

在当前经济转型的大背景下,中国工业企业要想在国际竞争中求得生存和发展,自主创新是关键。本文主要围绕我国企业自主创新绩效进行分析。关于企业自主创新的讨论在现有文献中并不少见,但仍存在诸多不足之处。本文提出了较为全面的研究思路来克服这些不足:在使用广义Cobb-Douglas生产函数作为企业创新绩效研究的基本方程的同时,将R&D的累积投入代理企业知识资本;着重讨论R&D内生性问题,结合我国工业企业的特点,对R&D行为决策进行建模分析找出合适的工具变量,从而避免了参数估计值的不一致性。本文实证部分使用了中国2005—2007年度近3万家产值在500万元以上的工业企业数据。研究表明:企业通过产品与技术革新等创新活动,提高了企业的产出水平,其投入产出弹性达到5.5%,修正了常规方法偏低的估计值。     

非等间隔动态面板数据模型：估计方法与应用实例

非等间隔动态面板数据模型由于相邻两期观测之间的时间长度不尽相同使得传统动态面板数据模型的估计方法失效,本文提出使用非线性最小二乘、最短距离以及它们的一步估计量对该模型进行估计,证明了这四个估计量的一致性和渐进正态性,同时借助蒙特卡洛模拟的方法验证了它们在有限样本中的估计精度,并且进一步使用所提出的估计量讨论了以往文献由于缺乏相应的估计方法而没有被研究或者充分讨论的问题,得到了一些新的结论。     

A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data

The trend in crop yields and yield variability affects food security and impact agricultural and food policies. Recent studies in this area have either focused only on one country or performed global analysis on a handful of crops. We provide the first worldwide analysis of trend and variability for 8088 country-crop yield series taken from the FAO database, employing a robust estimator to cope with the adverse statistical effects of outliers. More than half of the series display a slowdown in yield growth due to a closing of the gap between realised and attainable yields as well as to agricultural policies promoting more sustainable agricultural practices. Around one fourth of the series show also an increase in yield variability as a consequence of climate change and changes in farm management practices. Yield variability is highest in Eastern Europe, Central Asia and Middle East and North Africa, where food security may be threatened.