大数据背景下面板数据政策评估的估计和推断

研究目标:介绍大数据背景下基于面板数据模型的政策评估方法的最新进展与相关应用。研究方法:回顾双重差分法、合成控制法、面板数据方法、因子估计方法和机器学习方法这几类方法在估计面板数据因果效应方面的最新进展后，介绍现有研究中基于上述估计量的推断方法，最后报告已有文献对于不同方法的对比，并提供实证应用建议。研究发现:当实证应用问题中随时间变化的因子个数超过一个时，特别要关注基于双向固定效应的双重差分法的适用性。运用双向固定效应设定模型不恰当时，可考虑使用基于交互固定效应模型的因子模型类估计和推断方法。研究创新:从大数据时代的政策评估需求出发，梳理基于面板数据的因果效应估计和推断方法并给出应用建议。研究价值:为实证研究者提供了选择政策评估方法的参考指南。     

时空动态半参数变系数随机效应面板模型的估计

研究目标：克服半参数变系数面板模型中扰动项和因变量存在时空动态性问题。研究方法：提出一类更加一般化的时空动态半参数变系数随机效应面板模型，并构建截面似然估计量。研究发现：估计量具有良好的小样本性质，估计误差随着样本总量的提高而减小，在Case空间矩阵下，空间滞后和时空滞后系数的估计精度随空间复杂度的增大而降低，用该方法分析我国外商直接投资、知识产权保护与经济增长关系，进一步证实了模型的适用性。研究创新：证明了估计量满足一致性和渐近正态性，数值模拟考察了估计量的小样本性质。研究价值：拓展了现有半参数变系数空间面板模型的形式，增强了模型的适用性和解释力，有益于经济问题实证研究的开展。     

非线性动态面板模型的条件GMM估计

基于时间序列的实证分析已经证实,很多经济变量的动态调整过程都存在非线性的平滑转换机制.本文将传统的线性动态面板模型扩展为平滑转换的非线性动态面板模型,并基于对非线性参数的格点搜索,提出了一种简便易行的非线性动态面板模型估计程序--条件GMM估计,其估计量具有一致性.仿真实验结果显示,条件GMM估计量在有限样本下具有良好表现.同时,非线性动态面板模型的条件GMM估计还为在非线性框架下检验面板单位根创造了条件.     

半参数趋势阈值面板模型及其参数估计

半参数趋势面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有的研究中,半参数趋势面板模型考虑了时间趋势的非线性,但没有考虑政策等因素对参数的影响。本文将结构突变理论引入截面相关下的半参数趋势面板模型,并基于PPLE方法,建立了有效估计量和识别程序。通过仿真实验和实证应用,验证了对于含有突变点的半参数趋势面板模型,EPPLE方法的参数估计是有效的。     

层级数据空间误差自回归模型的估计方法研究

本文建立同时考虑空间误差自回归和嵌套随机效应误差分量的层级数据空间误差自回归模型,并推导最优权重GMM估计量,对空间自回归系数和误差项的方差进行估计。然后,定义对应的FGLS估计量,对层级数据空间误差自回归模型的总体回归系数进行估计。通过蒙特卡洛模拟,验证了所提出模型估计量的有限样本性质。模拟结果表明,本文提出的最优权重GMM估计量以及总体回归系数的GMM FGLS估计量有很好的小样本性质。     

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。     

具有异质性非参数时间趋势的面板数据模型的估计

本文为一类具有异质性非参数时间趋势的面板数据模型提出了一种简单估计方法。基于局部多项式回归的思想,首先去除数据中的时间趋势成分,然后由最小二乘法来估计公共系数,同时得到时间趋势函数的非参数估计。在一些正则条件下,研究了这些估计量的渐近性质,即在时间维度T和横截面维度n同时趋向无穷时,建立了各个估计量的渐近相合性和渐近正态性。最后通过蒙特卡洛模拟,考查了这种估计方法的有限样本性质。     

交互效应面板模型的EM算法和MCMC算法

交互效应面板模型是目前计量经济学前沿研究的热点,有着广阔的应用空间。但是对很多应用者而言,模型内的参数估计是一个非常棘手的问题。通常的Newton-Raphson算法在优化似然函数的过程中,常常会出现优化失败的情况。本文依据EM算法和MCMC算法理论,为应用研究者提供了一套获得参数估计值的流程。计算机上的试验证实两种估计方法都非常稳健可靠,并在很多情况下,差异不是很大。     

公司治理机制、交互效应与银行绩效——基于面板的个体固定效应模型

本文以上市银行为样本，应用面板的个体固定效应模型，不仅研究股权集中度、董事会特征与银行绩效间的关系，而且研究股权集中度与董事会特征的交互效应与银行绩效间的关系。研究发现：（1）董事会的规模、董事会独立性、董事会积极性都与银行绩效有显著的正相关；（2）股权集中度与银行绩效有显著的正相关关系；（3）股权集中度与董事会特征间存在交互效应，即股权集中度与董事会特征的交叉项与银行绩效有显著的负相关关系。     

空间面板误差模型随机效应的稳健检验

在空间面板误差模型中,由于空间误差项和随机效应项相关,构建随机效应检验统计量时无法直接采用传统的F检验。同时,如果面板数据在时间维度上存在序列相关,要检验随机效应将变得更加困难。本文主要研究在扰动项存在序列相关的情况下如何构建空间面板误差模型中随机效应的稳健检验统计量。相应的有限样本性质通过蒙特卡罗模拟给出。     

Two-step estimation of panel data models with censored endogenous variables and selection bias

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.     

Testing for Multicointegration in Panel Data with Common Factors*

This paper addresses the concept of multicointegration in a panel data framework and builds upon the panel data cointegration procedures developed in Pedroni [Econometric Theory (2004), Vol. 20, pp. 597–625]. When individuals are either cross‐section independent, or cross‐section dependence can be removed by cross‐section demeaning, our approach can be applied to the wider framework of mixed I(2) and I(1) stochastic processes. The paper also deals with the issue of cross‐section dependence using approximate common‐factor models. Finite sample performance is investigated through Monte Carlo simulations. Finally, we illustrate the use of the procedure investigating an inventories, sales and production relationship for a panel of US industries.     

Decomposition of Technological Change and Factor Bias in Indian Power Sector: An Unbalanced Panel Data Approach

Technological change and factor bias in the Indian power sector are analyzed using a translog cost function. Various components of technological progress and factor bias are identified and estimated, using a 21 year unbalanced panel data of Indian states and union territories. Heterogeneity across states is incorporated in the model using a variance component model. Appropriate corrections are made for unbalanced panel data. Empirical results show that the annual average rate of technological progress has been 2.4% for the country as a whole. Accumulation of knowledge and increasing scale are found to be the major factors contributing to technological progress. In contrast, the effects of factor price changes and fixed capital accumulation on technological progress have been unfavorable. Pure factor bias measure indicate saving in the use of fuel and labor, and increased use of materials. Tests are performed to check the curvature properties of the underlying technology.     

Testing for Error Correction in Panel Data*

This paper proposes new error correction‐based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small‐sample properties with small size distortions and high power relative to other popular residual‐based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for.     

Indirect inference for dynamic panel models

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and is shown to have superior finite sample properties to the generalized method of moment (GMM) and the bias-corrected ML estimator.     

Time on market and sales price of residential housing: A note

This note extends the study of Kalra and Chan (1994). A simultaneous TOBIT equations model is established to address the simultaneity nature of time on the market (TOM) and sales price (SP) in the presence of censored sample bias. We find that both TOM and SP are positively related to each other.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

Fixed effects estimation of structural parameters and marginal effects in panel probit models

Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper, I characterize the leading term of a large-T expansion of the bias of the MLE and estimators of average marginal effects in parametric fixed effects panel binary choice models. For probit index coefficients, the former term is proportional to the true value of the coefficients being estimated. This result allows me to derive a lower bound for the bias of the MLE. I then show that the resulting fixed effects estimates of ratios of coefficients and average marginal effects exhibit no bias in the absence of heterogeneity and negligible bias for a wide variety of distributions of regressors and individual effects in the presence of heterogeneity. I subsequently propose new bias-corrected estimators of index coefficients and marginal effects with improved finite sample properties for linear and nonlinear models with predetermined regressors.