经济转轨中我国地方政府税收竞争行为的悖论

在我国经济转轨过程中,地方政府之间存在大量关于吸引FDI的税收竞争,但在现有的理论研究中,对地方政府税收竞争基本沿用国家之间竞争的模型,没有引入地方竞争的特殊激励。本文首先分析我国地方政府税收竞争的形式及其原因,然后尝试通过建立一个考虑地方政府特殊激励的税收竞争理论模型分析,以此对我国地方政府参与税收竞争的行为进行分析,并得出结论:地方政府过度税收减免是一种双输的政策,惟一获利的只是外国资本;中央政府应采取干预措施限制和消除地方政府之间的税收竞争;官员政绩考核机制是我国地方政府税收竞争的深层激励等。     

收入与增长权衡:分权下地方政府努力方向研究

基于1999-2013年全国30省际数据,利用动态面板模型,考察了财政分权深化下地方政府的努力方向。结果表明:在财政分权初期,地方政府因获得更多的资源支配权,其积极性被充分调动出来,并极大促进了经济增长和地方财政收入的增加。但随着财政分权进一步深化,其对经济增长推动作用逐渐减弱,而对地方财税增加的推进作用依然存在,地方政府的努力方向由此产生扭曲,稳定并提高地方财税增速成为其首要目标。建立合理的地方政府考核制度,完善财政分权制度,对于稳定地方经济增长速度,突破中等收入陷阱至关重要。     

Local composite quantile regression estimation of time-varying parameter vector for multidimensional time-inhomogeneous diffusion models

This paper is dedicated to the study of the composite quantile regression (CQR) estimations of time-varying parameter vectors for multidimensional diffusion models. Based on the local linear fitting for parameter vectors, we propose the local linear CQR estimations of the drift parameter vectors, and verify their asymptotic biases, asymptotic variances and asymptotic normality. Moreover, we discuss the asymptotic relative efficiency (ARE) of the local linear CQR estimations with respect to the local linear least-squares estimations. We obtain that the local estimations that we proposed are much more efficient than the local linear least-squares estimations. Simulation studies are constructed to show the performance of the estimations proposed.     

中国地方政府债务的结构性风险

在或有债务不确定触发地方政府代偿的现实背景下,本文对地方政府显性债务和或有债务的结构性风险进行了模型刻画,给出了两类债务违约概率的显示解及其估计方法。基于地方政府报告的显性债务和审计署的有关公告等,本文对地方政府的显性债务和或有债务规模进行了结构性分解和估算,多视角估计了不同久期（或平均债务到期时间）下的两类债务的结构性风险状态,同时对比分析了其变动趋势。模型估计表明：地方政府债务风险的主要形态是或有债务的不确定触发,在部分时段内,地方政府的结构性代偿压力明显较大,但债券置换等旨在拉长债务久期的政策设计有助于缓释地方政府债务的信用风险边界,降低地方政府的结构性代偿压力。本文用具体数字揭示了地方政府债务的结构性风险状况及其变动趋势,以及地方政府债务久期之伸缩与其代偿压力之间的定量关系。     

局部空间自相关指标对比研究

本文研究了最常用的局部空间自相关指标;局部Moran指数和局部G系数,基于模拟的空间区域;设计了一些有代表性的空间聚集方案进行计算,比较了两种指标的探测结果,得出局部G系数要优于局部Moran指数的初步结论。     

融资约束、土地财政与地方政府投资行为

本文立足于中国特定的制度环境,从融资约束的角度建立了一个地方政府投资和土地财政关系的分析框架,采用地方政府2002-2010年间的面板数据,检验融资约束对地方政府投资的影响,从而为进一步的财政改革提供微观证据。本文的研究结果表明：（1）地方政府投资和土地出让收入呈显著正相关关系,这说明地方政府投资受到了融资约束,过度依赖于土地出让收入。（2）“事权”和“财权”不匹配所导致的财政缺口加大了地方政府融资约束,而金融发展则缓解了融资约束现象,财政缺口较小、金融发展水平较高的地区,政府投资与土地出让收入之间的相关性较小。最后,本文认为进一步的财税体制改革和金融市场发展可以改善地方政府的融资条件,促进地方政府更有效率地进行投资。     

地方财政收入的空间非均衡、收敛性与极化研究

利用1994—2012年的中国省际经济数据,对地方财政收入的空间非均衡特征、收敛性与极化进行实证研究,结果表明:地方财政收入具有显著的空间非均衡特征;地方财政收入空间分布的总体差距呈现缩小趋势,其中地区间差距是地方财政收入差距的主要来源;考虑空间相关性前,地方财政收入增长存在绝对β收敛和条件β收敛;考虑空间相关性后,地方财政收入增长的绝对β收敛速度大大上升,条件β收敛速度大大下降;地方财政收入空间极化程度呈现先上升后下降的趋势。     

Local Likelihood Estimation in Generalized Additive Models

ABSTRACT.  Generalized additive models are a popular class of multivariate non-parametric regression models, due in large part to the ease of use of the local scoring estimation algorithm. However, the theoretical properties of the local scoring estimator are poorly understood. In this article, we propose a local likelihood estimator for generalized additive models that is closely related to the local scoring estimator fitted by local polynomial regression. We derive the statistical properties of the estimator and show that it achieves the same asymptotic convergence rate as a one-dimensional local polynomial regression estimator. We also propose a wild bootstrap estimator for calculating point-wise confidence intervals for the additive component functions. The practical behaviour of the proposed estimator is illustrated through a simulation experiment.     

Local and Variable Bandwidths and Local Linear Regression

Two types of non-global bandwidth, which may be called local and variable, have been defined in attempts to improve the performance of kernel density estimators. In nonparametric regression, local linear fitting has become a method of much popularity. It is natural, therefore, to consider the use of non-global bandwidths in the local linear context, and indeed local bandwidths are often used. In this paper, it is observed that a natural proposal in the literature for combining variable bandwidths with local linear fitting fails in the sense that the resulting mean squared error properties are those normally associated with local rather than variable bandwidths. We are able to understand why this happens in terms of weightings that are involved. We also attempt to investigate how the bias reduction expected of well-chosen variable bandwidths might be achieved in conjunction with local linear fitting.     

One-step local quasi-likelihood estimation

Local quasi-likelihood estimation is a useful extension of local least squares methods, but its computational cost and algorithmic convergence problems make the procedure less appealing, particularly when it is iteratively used in methods such as the back-fitting algorithm, cross-validation and bootstrapping. A one-step local quasi-likelihood estimator is introduced to overcome the computational drawbacks of the local quasi-likelihood method. We demonstrate that as long as the initial estimators are reasonably good, the one-step estimator has the same asymptotic behaviour as the local quasi-likelihood method. Our simulation shows that the one-step estimator performs at least as well as the local quasi-likelihood method for a wide range of choices of bandwidths. A data-driven bandwidth selector is proposed for the one-step estimator based on the pre-asymptotic substitution method of Fan and Gijbels. It is then demonstrated that the data-driven one-step local quasi-likelihood estimator performs as well as the maximum local quasi-likelihood estimator by using the ideal optimal bandwidth.     

Kernel spline regression

The authors propose «kernel spline regression,» a method of combining spline regression and kernel smoothing by replacing the polynomial approximation for local polynomial kernel regression with the spline basis. The new approach retains the local weighting scheme and the use of a bandwidth to control the size of local neighborhood. The authors compute the bias and variance of the kernel linear spline estimator, which they compare with local linear regression. They show that kernel spline estimators can succeed in capturing the main features of the underlying curve more effectively than local polynomial regression when the curvature changes rapidly. They also show through simulation that kernel spline regression often performs better than ordinary spline regression and local polynomial regression.     

经济开放与地方财政支出

 本文在经典的中位选民需求函数模型基础上,构建了地方财政支出决定的动态空间面板模型,分析了经济开放对地方财政支出的影响。研究发现,经济开放的地方财政支出效应是多维和复杂的,经济开放引起了地方政府支出规模的扩大,外商投资扩大使地方支农支出、社会福利支出增加,贸易开放使地方文化和公检法司支出增加,支持了经济开放的补偿假说;同时,投资开放引起行政管理支出和财政补贴支出减少,贸易开放度的提升促进了地方财政基础设施、科教等支出的增加,并减少社会福利支出,又支持效率假说。经济开放对地方财政支出结构扭曲的矫正和强化效应并存,我国应在坚持开放的前提下,积极创造使经济开放的积极效应最大化、消极效应最小化的制度条件。     

Local M-estimation for Conditional Variance in Heteroscedastic Regression Models

In this article, we develop a local M-estimation for the conditional variance in heteroscedastic regression models. The estimator is based on the local linear smoothing technique and the M-estimation technique, and it is shown to be not only asymptotically equivalent to the local linear estimator but also robust. The consistency and asymptotic normality of the local M-estimator for the conditional variance in heteroscedastic regression models are obtained under mild conditions. The simulation studies demonstrate that the proposed estimators perform well in robustness.     

Local influence in binary regression models,and its correspondence with global influence

We develop local influence diagnostics for a general binary regression model,and apply these methods to case-weight perturbations in four examples. In addition, we illustrate the correspondence between case-deletion diagnostics and local case-weight perturbation slopes and curvatures. We demonstrate that local influence diagnostics can provide a more computationally efficient means for obtaining analogous information to that yielded by case-deletion diagnostics, which can be thought of as global influence perturbations. We also assess the global consistency of patterns of local influence using these data examples.     

Local CQR Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression

Summary.  Local polynomial regression is a useful non-parametric regression tool to explore fine data structures and has been widely used in practice. We propose a new non-parametric regression technique called local composite quantile regression smoothing to improve local polynomial regression further. Sampling properties of the estimation procedure proposed are studied. We derive the asymptotic bias, variance and normality of the estimate proposed. The asymptotic relative efficiency of the estimate with respect to local polynomial regression is investigated. It is shown that the estimate can be much more efficient than the local polynomial regression estimate for various non-normal errors, while being almost as efficient as the local polynomial regression estimate for normal errors. Simulation is conducted to examine the performance of the estimates proposed. The simulation results are consistent with our theoretical findings. A real data example is used to illustrate the method proposed.     

Robust estimation of multivariate regression model

This paper studies robust estimation of multivariate regression model using kernel weighted local linear regression. A robust estimation procedure is proposed for estimating the regression function and its partial derivatives. The proposed estimators are jointly asymptotically normal and attain nonparametric optimal convergence rate. One-step approximations to the robust estimators are introduced to reduce computational burden. The one-step local M-estimators are shown to achieve the same efficiency as the fully iterative local M-estimators as long as the initial estimators are good enough. The proposed estimators inherit the excellent edge-effect behavior of the local polynomial methods in the univariate case and at the same time overcome the disadvantages of the local least-squares based smoothers. Simulations are conducted to demonstrate the performance of the proposed estimators. Real data sets are analyzed to illustrate the practical utility of the proposed methodology. This work was supported by the National Natural Science Foundation of China (Grant No. 10471006).     

人口规模、“省直管县”与地方公共品供给的影响 ——来自湖北省市、县两级数据的经验证据

 本文在构建地方公共品最优供给模型基础上，运用湖北省县级和地市级面板数据就人口规模、“省直管县”对地方公共品供给的影响进行了实证检验。研究发现：基于Bordherding-Deacon常弹性拥挤函数的模型比较有效的刻画了地方公共品的供给；地市和县人口增加产生的“规模效应”大于“拥挤效应”；在县级公共服务需求的价格弹性为正，价格机制在县域公共品供给中是失灵的，而在地市级价格弹性为负，价格机制发挥着一定作用；无论地市级还是县级，公共品需求的收入弹性都大于1而富有弹性，符合“瓦格纳法则”；上级财政补助在改善地方公共服务中的作用不明显。     

Inference for Local Autocorrelations in Locally Stationary Models

For nonstationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a “V” shape during 1910–1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.     

Confidence Intervals Based on Local Linear Smoother

Point-wise confidence intervals for a non-parametric regression function in conjunction with the popular local linear smoother are considered. The confidence intervals are based on the asymptotic normal distribution of the local linear smoother. Their coverage accuracy is evaluated by developing Edgeworth expansion for the coverage probability. It is found that the coverage error near the boundary of the support of the regression function is of a larger order than that in the interior, which implies that the local linear smoother is not adaptive to the boundary in terms of coverage. This is quite unexpected as the local linear smoother is adaptive to the boundary in terms of the mean squared error.     

地方资源禀赋、制度环境与地方债发行

我国地方政府债务水平的高速增长引发了各界的警惕,而地方政府的发债动机、发债能力更是饱受质疑。本文利用2004-2012年我国地级市的发债数据,对政府发债动机及发债能力进行研究。我们发现,地方政府的投资冲动越强,地方政府发行债券的可能性越高;城市的财政赤字情况越严重,则地方政府发行债券的机会越低,而城市的经济规模、经济增速则对城市发债能力产生正面影响。在此基础上,我们进一步研究制度环境对城市发债行为的影响。研究表明,金融成熟度对城市特征产生了“放大”效应,发债双方的关系更加遵从“市场规则”;而“关系成本”的存在则打破了“市场规则”对地方政府的约束。我们的研究深化了对地方政府发债行为及地方政府债务风险的理解。