Panel data inference under spatial dependence

This paper focuses on inference based on the standard panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial autoregressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the standard panel data estimators that ignore spatial dependence can lead to misleading inference.     

人力资本空间特性的ESDA分析——基于山东省的实证研究

利用山东省2000-2010年17城市教育支出水平的面板数据,探究了山东省人力资本空间分布模式及其内在机理。总体来看山东省人力资本全域自相关特性并不显著,其空间分布趋向于离散、随机的分布模式;而局域空间自相关分析却表现出人力资本空间分布的异质性特点,其中以济青都市圈所辖城市为主的核心区域大都显示为正的空间自相关性,而外围区域大都显示出负的空间自相关性。     

中国产业结构调整的碳排放效应——基于STIRPAT扩展模型及空间面板数据的实证研究

利用1997—2010年我国省际面板数据,基于STIRPAT的扩展形式,运用空间面板模型实证分析了我国产业结构调整的碳排放效应。与以往研究不同的是,将空间自相关性纳入传统模型中,并且未采用简单的三次产业划分法描述产业结构,而是将生产部门细分为五类行业。结果表明:我国各省区的二氧化碳排放量存在空间维度上的依赖性和异质性,产业结构高级化进程受经济规律和发展实际的限制,这影响了减排效果的发挥;农业机械化的发展步伐不断加快,但工业发展的高能耗、高污染特征仍非常明显,农业和工业增加值占GDP比重分别与二氧化碳排放量显著正相关,其他行业比重调整的降碳作用有待更充分的激发。指出:我国应推进农业机械化与产业结构战略性调整相结合,促进以技术创新与进步为基础的产业内部结构升级;各区域要力求与周边地区协调、互助,共建以低碳排放为特征的产业体系。     

Spatial panel data analysis with feasible GLS techniques: An application to the Chinese real exchange rate

Recent panel data approaches stress the importance of the location interdependence. Little has been done in the Balassa-Samuelson literature accounting for spatial dependence in the panel data context that allows for spatial autocorrelation. By utilising the recently developed Kapoor et al. (2007) spatial panel feasible GLS methods, we find that the Balassa-Samuelson effect in the Chinese economy during 1985 until 2000 generally does not appear to hold. However, the black market exchange rate tends to be more compatible with the theory.     

中国绿色建筑面积时空演化及驱动机制研究

基于中国绿色建筑面积的面板数据,运用差异指数、自然断裂点法、空间自相关分析研究绿色建筑面积的时空演化,建立固定效应面板数据模型分析绿色建筑面积空间分布格局的驱动机制。结论如下:(1)绿色建筑面积绝对差异呈增大趋势,相对差异呈缩小趋势;(2)我国绿色建筑面积的演化以路径依赖型为主,处于低水平区和中低水平区的省域数量较多;(3)绿色建筑面积存在显著的正空间自相关性,低低聚集状态为主,形成以浙江、安徽、上海以及江苏为核心,向相邻省域扩散的高值热点区域,低值冷点区域则呈向西北扩散趋势;(4)绿色建筑面积空间格局的形成是经济核心驱动、人口直接驱动、政策外部驱动、自然基础驱动和市场内部驱动共同作用的结果。     

基于空间计量的房地产价格影响因素分析

现有文献大都认为各个地区的房地产价格是相互独立的,而事实上,空间依赖的存在会使得回归结果是有偏的。本文首先利用全局空间自相关指标(Moran’s I统计量)和局部空间自相关指标(LISA指标)检验我国房价的空间自相关,结果显示我国房价存在空间自相关。然后利用1999-2009年的省际面板数据,运用广义空间面板数据模型分析全国范围以及东、中、西部分区域房价,结果表明,在考虑了房价的空间相关性后,我国各区域房价的影响因素已不尽相同,东部地区房价基本完全由空间因素决定,西部地区房价由供给和需求等因素决定,而全国范围及中部地区房价受两者的共同影响,利率和汇率的变化对我国房价无显著影响。     

A panel data model with interactive effects characterized by multilevel non-parallel factors

Current research only considers parallel factors in factor models. In this article, we provide an algorithm based on cross principal component analysis that identifies and estimates a panel data model with interactive effects characterized by multilevel and non-parallel factors. The simulation results show that our estimator is consistent, converges quickly and outperforms other estimators that identify the factor structure incorrectly.     

Predicting land use allocation in France: A spatial panel data analysis

Predictions of future land use areas are an important issue as land use patterns significantly impact environmental conditions (biodiversity, water pollution, soil erosion, and climate change) as well as economic and social welfare. In order to improve the prediction accuracy of aggregated land use share models, we propose in this paper a methodological contribution by controlling for both unobserved individual heterogeneity and spatial autocorrelation. Our model is a land use shares model applied to aggregated data in France. Our dataset is a panel which covers both time series observations from 1992 to 2003 and cross-sectional observations by Département (equivalent to NUTS3 regions). We consider four land use classes: (1) agriculture, (2) forest, (3) urban and (4) other use. We investigate the relation between the areas in land in different alternative uses and economic and demographic factors influencing land use decisions. Based on the comparison of prediction accuracy of different model specifications, our findings are threefold: First, controlling for both unobserved individual heterogeneity and spatial autocorrelation outperforms any other specification in which spatial autocorrelation and/or individual heterogeneity are ignored. Second, accounting for cross-equation correlations does not seem to improve the prediction performances and finally, ignoring individual heterogeneity introduces substantial loss in prediction accuracy.     

Cost efficiency and banking performances in a partial universal banking system: application of the panel smooth threshold model

This paper studies the cost efficiency of bank in a partial universal banking system (PUBS), Taiwan. Instead of assuming one common technology in the bank cost function, two technologies are assumed to be imbedded in the cost function. Fee revenues are used as threshold to divide the banks into two technologies. A bank whose fee revenues exceeding the threshold is designated as universal bank technology while falling below the threshold is designated as traditional deposit-loan technology. The panel smooth transition model is adopted, which allows banks to smoothly adjust between the two technologies. Two criteria are suggested, overbanking and the trend-toward-fee revenues, to assess the new model's performances. With respect to scale economies, the results do find a panel smooth transition model yield more reasonable results than the conventional OLS and random effect of panel data approach. Based on the panel smooth transition model, the optimal fixed asset size is around ten billion New Taiwan dollars.     

城市化对房价的影响:线性还是非线性?——基于四种面板数据回归模型的实证分析

文章从线性和非线性两个角度分析了中国城市化进程对房价的影响。通过对各省历年房价和城市化的核密度估计空间分布分析,发现城市化和房价之间存在明显的正相关性,并且各省份的城市化和房价水平存在"双峰"分布特征和空间相关性。这说明在分析城市化对房价的影响时应考虑可能的门限效应和空间溢出效应这两种非线性关系。据此,文章基于中国30个省份1998-2009年的面板数据,使用普通面板回归、空间面板回归、门限面板回归和平滑门限面板回归这四种模型进行分析发现,城市化水平对本地区和相邻地区的房价均具有显著的促进作用,且在经济增长水平较高、人力资本集聚的地区,城市化对房价的促进作用更加显著。     

Instrumental variable estimation of a spatial autoregressive panel model with random effects

This paper extends the instrumental variable estimators of Kelejian and Prucha (1998) and Lee (2003) proposed for the cross-sectional spatial autoregressive model to the random effects spatial autoregressive panel data model. It also suggests an extension of the Baltagi (1981) error component 2SLS estimator to this spatial panel model.     

FDI对中国二氧化碳排放的影响——基于省域面板数据的空间计量

采用空间计量方法,利用1998—2010年中国省域面板数据,研究中国省域FDI与CO2排放量的关系。研究结果表明:中国省域FDI和CO2排放量都存在显著的空间自相关性;FDI高值集聚区一般是中国CO2排放量低值集聚区,FDI低值集聚区一般是中国CO2排放量高值集聚区。采用空间误差模型和空间滞后模型研究FDI对中国CO2排放量的影响。结果显示:FDI在地理上的集聚有助于降低中国的CO2排放量,"污染天堂假说"在中国并不成立。最后,基于实证结果提出了相关的政策建议。     

经济发展水平、产业结构与碳排放强度——中国省级面板数据分析

利用我国29个省市自治区1995—2007年的面板数据,本文分析了二氧化碳排放强度同经济发展水平及产业结构之间的关系。通过多项计量检验,比较了不同计量模型之后,笔者选取了能够修正面板异相关和自相关的可行的广义最小二乘法(FGLS)模型。研究表明,(1)碳强度同人均GDP之间存在"N"形关系。(2)第二产业比重同碳强度存在正相关关系,即第二产业比重越高,二氧化碳排放强度就越高。(3)对经济发展与碳排放之间关系进行的情景分析表明,如果产业结构不改变,没有实施另外的政策,经济增长速度本身难以引致碳排放强度的大幅下降,2020年二氧化碳排放强度下降40%~45%的目标难以实现。     

A spatial model of corporate tax incidence

Using a unique, self-compiled data-set on international tax rates, we explore the link between taxes and manufacturing wages for a panel of 66 countries over 25 years. We find, controlling for other macroeconomic variables, that wages are significantly responsive to corporate taxation. Higher corporate tax rates depress wages. Using spatial modelling techniques, we also find that tax characteristics of neighbouring countries, whether geographic or economic, have a significant effect on domestic wages. We test for, and reject, spatial autocorrelation in our model using a modification of the Moran-I test statistic that accounts for country-specific fixed effects in a panel data setting. Our article fits in with the new economic geography literature as well as the urban economics literature which attempt to explain the spatial distribution of wages.     

Misspecification in Dynamic Panel Data Models and Model‐Free Inferences

This paper discusses the issue of model misspecification and model‐free methods in dynamic panel data analysis. We primarily review existing results, but also provide several new results. When the dynamics are homogeneous, we show that several widely used estimators for panel first‐order autoregressive AR(1) models converge to first‐order autocorrelation, even under misspecification. Under heterogeneity, these estimators converge to the ratio of the means of the first‐order autocovariances and variances. We also discuss the estimation of autocovariances, the estimation of panel AR(∞) models, and the estimation of the distribution of the heterogeneous mean and autocovariances.     

A model of attrition and income for dynamic longitudinal surveys

This paper develops a random effects model of attrition and income applicable to a dynamic longitudinal survey such as the Survey of Income and Program Participation. Based on the partial dynamic adjustment hypothsis, this study finds that the speed of adjustment of income is not instantaneous as suggeted in many past studies using annual panel data. Also, the short-run coefficients are much smaller than their long-run counterparts, and the coefficient estimates differ substantially among individuals with divergent socioeconomic characteristics. Caution should therefore be exercised when applying dynamic panel data to models with the assumption of an instantaneous speed of adjustment.     

Self‐employment Dynamics in Australia and the Importance of State Dependence

Using Australian labour force data, both cross‐sectional and longitudinal, this study examines the dynamics of self‐employment with a particular focus on workers transitioning between self‐employment and salaried employment, and the extent to which self‐employment is the result of workers’ observed and unobserved characteristics or is instead determined by their prior employment experience itself. Probability models of self‐employment using both pooled‐panel probit and dynamic random‐effects panel probit methods are estimated, and the results are found to be extremely sensitive to the differences in the econometric methods. Once unobserved heterogeneity and initial conditions are controlled for in the dynamic model, the importance of observed characteristics in determining self‐employment is greatly diminished. Instead, workers’ past experience in self‐employment (as opposed to salaried employment) is found to have a large favourable effect on their future self‐employment prospects. The influence of this state dependence is also considerably more important in determining self‐employment outcomes than salaried ones. Despite establishing the importance of state dependence, however, what this effect implies about why individuals choose to become self‐employed or the role that self‐employment plays in the labour market remains unresolved.     

Exports and economic growth in Central and East European countries during transition

This paper quantifies the contribution of exports to economic growth in Central and East European countries (CEECs) during transition. Two theoretical models are examined: the first is based on an aggregate production function which includes exports as an additional ‘input’; while the second is based on a two-sector (exports and non-exports) model where exports provide positive externalities in non-export production. Each model is estimated with both fixed and random effects using panel data. Results show that the random effects model is preferred and that exports have a significant impact on economic growth.     

金融发展与医药制造业发展间关系的实证分析——基于我国省级面板数据

利用1995—2008年我国29个省级地区的面板数据,利用面板数据的固定效应变截距模型和固定效应变系数模型,对金融发展与医药制造业发展之间的关系进行了实证分析。研究结果表明:我国金融发展对医药制造业发展的影响具有显著的地区差异;在西部地区,前者对后者具有显著的积极影响;在东部地区,前者对后者具有正面影响,但统计上不显著;在中部地区,前者对后者具有负面影响,但统计上不显著。最后提出应充分发挥政策性金融在医药制造业发展中的作用。     

Some Stylized Facts about International Trade Flows

Since the mid‐1990s there has been a proliferation of empirical models in the trade literature. Focus has ranged from the effect of particular explanatory variables to improved econometric techniques. However, there appears to be a lack of analyses on large international trade datasets aiming at describing the “stylized facts” of observed bilateral trade flows. Uncovering them is crucial as any empirical econometric model should reflect the basic properties of the data generating process. On the basis of a large panel dataset this paper finds that bilateral trade, despite being often unbalanced, tends to be reciprocal and persistent, and that the extensive margin of trade must not be disregarded. Moreover, bilateral trade flows are probably best modeled as a mixed panel of stationary and non‐stationary processes. The stationary vs non‐stationary separation of these flows, although not random, does not appear to be related to any common characteristics of the trading partners.