基于互联网大数据的CPI舆情指数构建与应用

研究目标：基于互联网大数据构建CPI舆情指数辅助预测CPI。研究方法：提出了一种构建CPI低频与高频舆情指数的统计方法,并通过选用2006年6月至2015年12月的数据验证了该方法的有效性。研究发现：相关关键词的搜索热度指标具有领先CPI的预测作用,依此建立的CPI舆情指数有助于改进CPI预测精度。研究创新：揭示了基于相关关键词的搜索热度指标与CPI的非线性关系,提出了一种基于门限回归的CPI低频舆情指数构建方法;使用动态因子模型估计出了CPI高频舆情指数。研究价值：预测CPI时可辅助利用基于大数据构建的CPI低频与高频舆情指数信息。     

Productivity efficiencies in Ukrainian polyclinics: lessons for health system transitions from differential responses to market changes

Ukraine’s recent elections revealed deep divisions between eastern regions, which favored central economic planning, and western regions, which preferred more free market reforms. This study compares polyclinics in Ukraine to see if the inflexibility of Soviet-style planned economies results in lower economic efficiency in eastern regions. Using data from two geopolitical regions, Data Envelopment Analysis (DEA) scores for polyclinic efficiencies are modeled as a function of demographic and economic determinants. Surprisingly, results indicate that polyclinics in western Ukraine are less efficient. Possible explanations, including case mix intensity, responsiveness to local preferences, physician entrepreneurial behavior and a legacy of inequitable funding, are discussed.

我国区域能源消费与经济增长关系的实证分析——基于面板单位根检验与协整检验的研究

文章运用Panel Data的单位根检验、协整检验及误差修正模型对我国东、中、西部三个地区的国内生产总值和能源消费之间的关系进行论证分析。结果表明,我国三个地区的GDP和能源消费之间都存在长期协整关系。通过进一步的Granger因果关系检验发现：在5%显著性水平下,东部地区存在由能源消费到GDP单向的短期因果关系,西部地区短期内则存在能源消费与GDP的双向因果关系。不仅如此,长期来看,西部地区存在由GDP到能源消费的单向因果关系,中部地区的检验结果不同于东部和中部地区,无论是在长期还是短期,中部地区的GDP和能源消费之间不存在任何长期或短期的因果关系。最后提出相应的政策建议。     

Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.     

The Application of Mathematical Programming Approaches to Estimating Container Port Production Efficiency

Container terminal production is both an important and complicated element in the contemporary global economy. This paper aims to evaluate the efficiency of the world’s most important container ports and terminals using the two alternative techniques of Data Envelopment Analysis (DEA) and the Free Disposal Hull (FDH) model. The results give an insight into the current efficiency ranking of the world’s major container ports and terminals. They also confirm expectations that the available mathematical programming methodologies lead to different results and that appropriate variable definition of input and output factors is a crucial element in meaningful applications of DEA and FDH. It is also concluded that the availability of panel data, rather than cross-sectional data would greatly improve the validity of the efficiency estimates derived from all the mathematical programming techniques applied.JEL Classification:C61, D24, E23, L23, L25, L92     

基于ESDA-GWR的粮食单产及其驱动因子的空间异质性研究

在耕地面积扩展有限和城市化高速发展的背景下,粮食单产的区域差异对我国粮食安全具有重要意义。以我国31省（市、自治区）为研究单元,借助ESDA和GWR模型,结合我国各地自然条件的差异及其经济发展类型,揭示我国粮食单产的区域差异及其驱动因子的空间异质性。结果表明：1999—2008年,中西部地区粮食单产的增产对全国粮食产量的贡献越来越显著,75%的粮食单产增长潜力较大的区域集中在中西部;粮食单产存在着显著的空间相关性,各省域粮食生产重心发生移动,由传统的＂东部为主＂逐渐＂北上西进＂,向中西部地区欠发达省份集中;不同区域粮食单产的驱动因素呈现为一种非均衡联动的局域性特征,化肥施用量对粮食单产的影响在两个时间断面中差异最大,1999年回归系数全为正值,而在2006—2008年出现负值,这表明部分地区化肥对粮食单产的边际效应已经达到递减阶段。因此,应结合我国不同区域当前粮食单产的现状和驱动因子的效应机理,积极采取相应的有效措施以保障我国的粮食安全。     

中国钢铁、汽车、房地产行业运行效率研究（2000-2004）——基于33家上市公司面板数据的DEA实证分析

本文运用33家上市公司面板数据和DEA方法的改进模型，从投入产出结合角度对我国2000-2004年间钢铁、汽车、房地产三大行业的运行效率进行实证研究，建立了运行效率评价模型（G0）及相应投影模型和评价指标体系；计算模型，求出各行业样本公司每年的运行效率指数、投影数据并推算出行业运行效率指数等其他相关数据，定量分析5年间三大行业运行效率的变化趋势，并进行比较研究，得出与事实相吻合的七个结论。     

Performance Measurement in Government Service Provision: The Case of Police Services in New South Wales

The NSW Government is implementing a financial framework which is designed to encourage government service providers to become more efficient and effective. NSW Treasury is using Data Envelopment Analysis (DEA) to measure the efficiency of major government service providers, such as police, courts and hospitals. This paper outlines the progress in implementing the new financial framework and illustrates the way NSW Treasury will use DEA to help improve the efficiency of government service providers by describing an analysis of the NSW Police Service. The results suggest that NSW police patrols (local police districts) could, on average, reduce input usage by 13.5 percent through better management, and by 6 percent if the patrols could be restructured to achieve the optimal scale. Results also indicate that differences in operating environments, such as location and socioeconomic factors, do not have a significant influence upon the efficiency of police patrols.     

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.