经济适用房空间布局分析——以北京市为例

经济适用房的建设和布局对城市空间结构和居住者出行具有重要影响.首先采用Kernel密度估计方法计算了北京不同时期经济适用房在不同空间上的建设规模密度,分析其空间布局特征;其次,从经济适用房与地价、交通和主要就业地的相互关系分析经济适用房空间布局的演变.研究表明,北京经济适用房空间布局经历了逐步调整的过程,前期主要分布在城市北部城乡结合地带,后期考虑居民交通出行和到工作地方便等因素,在城市四周较均匀的配置和建设.总体来说,北京市经济适用住房基本分布在城市郊区,就业地与居住地相距较远的矛盾依然突出.建议今后经济适用房空间布局应更加注重主要就业地和居民交通出行的最佳匹配.     

A unified approach to validating univariate and multivariate conditional distribution models in time series

Modeling conditional distributions in time series has attracted increasing attention in economics and finance. We develop a new class of generalized Cramer–von Mises (GCM) specification tests for time series conditional distribution models using a novel approach, which embeds the empirical distribution function in a spectral framework. Our tests check a large number of lags and are therefore expected to be powerful against neglected dynamics at higher order lags, which is particularly useful for non-Markovian processes. Despite using a large number of lags, our tests do not suffer much from loss of a large number of degrees of freedom, because our approach naturally downweights higher order lags, which is consistent with the stylized fact that economic or financial markets are more affected by recent past events than by remote past events. Unlike the existing methods in the literature, the proposed GCM tests cover both univariate and multivariate conditional distribution models in a unified framework. They exploit the information in the joint conditional distribution of underlying economic processes. Moreover, a class of easy-to-interpret diagnostic procedures are supplemented to gauge possible sources of model misspecifications. Distinct from conventional CM and Kolmogorov–Smirnov (KS) tests, which are also based on the empirical distribution function, our GCM test statistics follow a convenient asymptotic $N(0,1)$ distribution and enjoy the appealing “nuisance parameter free” property that parameter estimation uncertainty has no impact on the asymptotic distribution of the test statistics. Simulation studies show that the tests provide reliable inference for sample sizes often encountered in economics and finance.     

On Testing for the Nullity of Some Skewness Coefficients

Three tests for the skewness of an unknown distribution are derived for iid data. They are based on suitable normalization of estimators of some usual skewness coefficients. Their asymptotic null distributions are derived. The tests are next shown to be consistent and their power under some sequences of local alternatives is investigated. Their finite sample properties are also studied through a simulation experiment, and compared to those of the √ b ₂-test.     

A comprehensive evaluation of macroeconomic forecasting methods

We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.     

Non-parametric and semi-parametric asset pricing: An application to the Colombian stock exchange

We estimate non-parametrical one-factor and three-factor international Capital Asset Pricing Models (CAPM) and find strong evidence for rejecting the linear CAPM specification. Furthermore, we find inconsistent linear betas for a series of stocks in the Colombian stock exchange (BVC), supporting the hypothesis of better and consistent fit of non-parametrical versions of the CAPM.     

Spatial interaction modeling of interregional commodity flows

Drawing from both the spatial price equilibrium theoretical framework and the empirical literature on spatial interaction modeling, this paper expands models of interregional commodity flows (CFs) by incorporating new variables and using a flexible Box-Cox functional form. The 1993 US commodity flows survey provides the empirical basis for estimating state-to-state flow models for 16 commodity groups over the 48 continental US states. The optimized Box-Cox specification proves to be superior to the multiplicative one in all cases, while selected variables provide new insights into the determinants of state-to-state CFs.     

七大城市群创新能力的区域差距及其分布动态演进

研究目标：京津冀、长三角、珠三角、长江中游、成渝、中原以及哈长城市群创新能力的区域差距及其时空动态演进。研究方法：运用Dagum基尼系数、Kernel密度估计、空间Markov链进行实证研究。研究发现：七大城市群创新能力空间差距呈缩小趋势但依然显著,区域间差距是总体差距的主要来源;其创新能力分布呈不同的极化现象与趋势;其创新能力发展具有显著的空间依赖性,邻域创新环境的优劣对周边城市创新水平的向上转移产生非同步、差异化影响。研究创新：挖掘七大城市群创新能力区域差距来源,并通过科学揭示其时空演变规律为城市群创新差距提供空间解释。研究价值：对缩小城市群创新能力区域差距,构建创新驱动的区域经济发展支撑点具有重要的理论和现实意义。     

A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research

Mediation as a theory testing approach has witnessed considerable adoption among Operations Management (OM) researchers. Although mediation-testing methods have evolved tremendously in the past decade, their dissemination in the OM field has not seen parallel growth. These advanced techniques facilitate the testing of existing and complex hypotheses in a more precise manner. With the intent of critically evaluating existing and alternative methods for conducting mediation analysis needed to support sophisticated empirical research, this paper first reviews OM studies that tested for mediation in the past eleven years (2002–2012) from top-tier OM journals. Four commonly used mediation approaches were identified. Based on principles of good theory building, type of mediation model, and properties of empirical data, we evaluate the existing methodologies and make recommendations on how to improve the rigor of OM mediation testing. Using published OM studies in top journals as examples, we then illustrate the relevance and advantages of these recommendations, as well as their ease of use. Furthermore, we empirically show that more robust and insightful results can be achieved by adopting these techniques, which in turn have the promise of leading to better theory building and testing in the field of operations management.     

A multilevel, identity-based approach to leadership development

A frontier of leadership development is examined involving the respective roles of levels-of-analysis and identity in constructing an integrated development system. An approach is described in which individual and relational leadership identities are the focus of developmental efforts at lower organizational levels (e.g., individual contributor and first-level supervisor) but collective identities become the focus at higher levels (e.g., general manager and above). The separate areas of levels-of-analysis and leader identities are first discussed in terms of their respective relevance to leadership development. These are then discussed jointly in elaborating on a proposed development approach that integrates across organizational levels as well as levels of development (i.e., leader development and leadership development). In developing collective leadership identities, processes that involve participants in engaging across boundaries (functional, hierarchical, geographical) are recommended.     

A coupled Markov chain approach to credit risk modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.     

A Review on Modal Clustering

In spite of the current availability of numerous methods of cluster analysis, evaluating a clustering configuration is questionable without the definition of a true population structure, representing the ideal partition that clustering methods should try to approximate. A precise statistical notion of cluster, unshared by most of the mainstream methods, is provided by the density‐based approach, which assumes that clusters are associated to some specific features of the probability distribution underlying the data. The non‐parametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the modes of the density function. An appealing implication is that the ill‐specified task of cluster detection can be regarded to as a more circumscribed problem of estimation, and the number of clusters is also conceptually well defined. In this work, modal clustering is critically reviewed from both conceptual and operational standpoints. The main directions of current research are outlined as well as some challenges and directions of further research.     

非参数核密度估计在异方差模型中的应用

针对非参数核密度估计中最优窗宽的选择在实际建模中的不足,提出了一个新的最优窗宽选择的迭代方法,克服了使用传统的经验法则所带来的局限性。并在此基础上用一种新的非参数核密度估计ML方法应用到了中国股票市场,通过与极大似然估计对比论证了此方法的有效性和可行性。实证分析表明,通过与实际值的模拟对比,运用非参数估计技术得到上证指数日收益率的拟合值要优于极大似然估计的拟合值。     

基于广义交叉谱密度方法的线性和非线性格兰杰因果关系检验

鉴于现行的均值格兰杰因果关系检验或者无法检验非线性的格兰杰因果关系,或者存在“维数灾难”问题,我们利用Chung 和 Hong (2007) 的广义交叉谱方法提出了一个能统一检验线性和非线性均值格兰杰因果关系的检验统计量。我们的广义交叉谱检验统计量渐近服从一个标准正态分布,它不但能考虑所有滞后阶的信息,而且避免了“维数灾难”问题。蒙特卡罗试验结果表明广义交叉谱检验具有良好的有限样本表现。     

A two-step approach to account for unobserved spatial heterogeneity

A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.     

Mining the past to determine the future: Problems and possibilities

Technological advances mean that vast data sets are increasingly common. Such data sets provide us with unparallelled opportunities for modelling and predicting the likely outcome of future events. However, such data sets may also bring with them new challenges and difficulties. An awareness of these, and of the weaknesses as well as the possibilities of these large data sets, is necessary if useful forecasts are to be made. This paper looks at some of these difficulties, using illustrations with applications from various areas.     

Forecasting temperature to price CME temperature derivatives

This paper seeks to forecast temperatures in US cities in order to price temperature derivatives on the Chicago Mercantile Exchange (CME). The CME defines the average daily temperature underlying its contracts as the average of the maximum and minimum daily temperatures, yet all published work on temperature forecasting for pricing purposes has ignored this peculiar definition of the average and sought to model the average temperature directly. This paper is the first to look at the average temperature forecasting problem as an analysis of extreme values. The theory of extreme values guides model selection for temperature maxima and minima, and a forecast distribution for the CME’s daily average temperature is found through convolution. While univariate time series AR-GARCH and regression models generally yield superior point forecasts of temperatures, our extreme-value-based model consistently outperforms these models in density forecasting, the most important risk management tool.     

A general framework for frontier estimation with panel data

The main objective of the paper is to present a general framework for estimating production frontier models with panel data. A sample of firms i = 1, ..., N is observed on several time periods t = 1, ... T. In this framework, nonparametric stochastic models for the frontier will be analyzed. The usual parametric formulations of the literature are viewed as particular cases and the convergence of the obtained estimators in this general framework are investigated. Special attention is devoted to the role of N and of T on the speeds of convergence of the obtained estimators. First, a very general model is investigated. In this model almost no restriction is imposed on the structure of the model or of the inefficiencies. This model is estimable from a nonparametric point of view but needs large values of T and of N to obtain reliable estimates of the individual production functions and estimates of the frontier function. Then more specific nonparametric firm effect models are presented. In these cases, only NT must be large to estimate the common production function; but again both large N and T are needed for estimating individual efficiencies and for estimating the frontier. The methods are illustrated through a numerical example with real data.     

出口贸易、工业碳排放效率动态演进与空间溢出

以2003~2012年我国30个省级经济单元为研究对象,从技术进步视角出发,利用包含非期望产出的SBM模型测算各省份工业碳排放效率,运用非参数Kernel密度估计方法研究各省份出口贸易和工业碳排放效率的动态演进过程,构建空间杜宾模型进一步考察出口贸易对工业碳排放效率的影响。研究结果表明：我国东部地区的工业碳排放效率最高,依次为中部、西部地区和东北综合经济区;核密度曲线展现出口贸易整体处于上升态势,工业碳排放效率呈现双峰趋同;在考虑空间因素后,出口贸易对本地区的工业碳排放效率改善产生促进作用,但对其他地区的工业碳排放效率增长产生抑制作用,也有碍于所有地区碳排放效率的提升。     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

中国五大城市群金融发展的空间差异及分布动态：2003~2013年

本文基于2003~2013年城市金融数据,采用Dagum基尼系数、核密度非参数估计方法实证考察了中国五大国家级城市群金融发展的空间差异及分布动态。研究结论如下,五大城市群总体、京津冀、长三角、成渝城市群金融发展水平呈总体上升趋势,珠三角、长中游城市群金融发展水平呈总体下降趋势。城市群金融发展具有显著的空间差异,其中京津冀城市群区域内差异最大,长三角与长中游城市群的区域间差异最大,区域间差异是总体差异的主要来源。各城市群金融发展区域内差异、区域间差异的演变趋势不一致,绝对差异与相对差异的演变趋势也不一致。城市群金融发展具有显著的梯度效应,呈现两极分化或多极分化趋势。