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。     

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。     

操纵性应计模型的一个改进——基于动态面板估计的实证分析

使用动态面板对操纵性应计模型进行估计可以克服截面数据模型的内生性估计偏误。采用1999—2011年中国A股市场相关数据,对动态面板模型和其他模型的检测能力进行比较,结果表明：在对第Ⅱ类错误进行检验时,动态面板模型检测能力显著优于其他模型;在对第1类错误、审计师意见检验时,动态面板模型检测能力与其他模型接近。     

财政社会性支出对经济增长的影响——基于动态面板数据模型的实证分析

广义矩法能够有效检验动态面板数据模型，模型回归结果表明我国30个省市1997-2011年财政社会性支出与地方经济增长显著正相关，但拉动作用微弱，财政社会性支出各组成部分对经济增长的作用各异，应当继续增加财政社会性支出在整个财政支出中的比重，并优化财政支出结构。     

城镇化与产业生态化的互动关系——以西北五省(区)为例

基于2000-2011年西北五省的面板数据,建立动态面板数据模型,并运用面板数据模型协整检验、因果检验以及广义矩估计,来研究城镇化与产业生态化的互动发展关系。研究发现,城镇化与产业生态化之间存在着协整关系,可以通过误差纠正机制来维持城镇化与产业生态化的长期均衡发展。从短期来看,城镇化和产业生态化之间的因果关系很难显现;从长期来看,城镇化是产业生态化的原因,城镇化每提升1个百分点,产业生态化水平就会提升1.29个百分点。最后提出了西北地区突破经济增长生态瓶颈约束,以及促进经济可持续发展和生态文明建设的有效路径。     

空间动态面板模型拟极大似然估计的渐近效率改进

Lee和Yu(2008)研究了一类同时带个体与时间固定效应的空间动态面板模型的拟极大似然估计量的大样本性质.本文说明当扰动项非正态时,拟极大似然估计量的渐近效率可以被进一步提高.为此,我们构造了一组合待定矩阵且形式一般的矩条件以用来包含对数似然函数一阶条件的特殊形式.从无冗余矩条件的角度,选取最优待定矩阵得到了最佳广义矩估计量.本文证明了当扰动项正态分布时,最佳广义矩估计量和拟极大似然估计量渐近等价;当扰动项非正态分布时,广义矩估计量具有比拟极大似然估计量更高的渐近效率.Monte Carlo实验结果与本文的理论预期一致.     

非财务利益相关者与企业现金持有的动态模型研究

文章采用中国制造业上市公司2007—2019年面板数据,通过构建一个动态调整模型,运用广义矩估计(GMM)从动态视角实证分析非财务利益相关者(如供应商、顾客和员工)如何影响公司现金持有.结果表明:供应商对中国制造业上市公司现金持有影响显著且负相关,顾客对现金持有影响显著且正相关,员工对现金持有的影响不明确.     

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.     

金融发展对经济增长的影响:基于中国省级面板数据的实证检验

为了验证我国近年来金融发展对经济增长影响的相关结果,采用了一个普通的面板数据模型,收集1985～2006年中国各省份的数据,所用的估计手段既包括面板数据的固定效应方法(FE)和随机效应方法(RE),还包括广义矩估计(GMM)和极大似然估计(MLE),从而大大提高了估计结果的可信度。实证结论表明:我国金融发展对经济增长的影响为正,这些正向影响基本上能够通过显著性检验;我国区域经济增长存在条件收敛;实物资本投资对经济增长的影响始终都是显著为正的;人力资本和劳动力对经济增长的影响力显著为负。     

FDI技术溢出与挤出效应——基于DEA与湖北省面板数据的实证分析

首先运用基于DEA的Malmquist生产率指数对湖北省各地区全要素生产率进行测量和分解,然后我们分别建立FDI技术溢出测量模型和扩展Panama Teanravisitsagoo的绝对挤出效应模型,对湖北省各地区的面板数据运用SUR(似然不相关回归)加权的方法,进行广义最小二乘估计。最终分别得出面板数据的技术溢出和挤出效应的变系数模型。通过实证分析,我们可以首次得出湖北省FDI的技术溢出效应不太明显以及FDI对内资的挤出效应具有内在逻辑的一致性。     

Testing a linear dynamic panel data model against nonlinear alternatives

The most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual- and/or time-specific effects. The consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model. In this paper, a new class of residual-based tests is proposed for checking the validity of dynamic panel data models with both large cross-sectional units and time series dimensions. The individual and time effects can be fixed or random, and panel data can be balanced or unbalanced. The tests can detect a wide range of model misspecifications in the conditional mean of a dynamic panel data model, including functional form and lag misspecification. They check a large number of lags so that they can capture misspecification at any lag order asymptotically. No common alternative is assumed, thus allowing for heterogeneity in the degrees and directions of functional form misspecification across individuals. Thanks to the use of panel data with large $N$ and $T$, the proposed nonparametric tests have an asymptotic normal distribution under the null hypothesis without requiring the smoothing parameters to grow with the sample sizes. This suggests better nonparametric asymptotic approximation for the panel data than for time series or cross sectional data. This is confirmed in a simulation study. We apply the new tests to test linear specification of cross-country growth equations and found significant nonlinearities in mean for OECD countries’ growth equation for annual and quintannual panel data.     

The relationship between development, investments, insecurity and social conditions in Colombia: a dynamic approach

In this paper, we investigate the relationship between economic development, investments, savings, insecurity and social conditions in Colombian departments. Using a dynamic heterogeneous panel analysis, we study the effects of insecurity and social conditions on economic development through an estimation of panel data cointegration techniques. The models applied in this study suggest a long-term relationship among economic development, investments, savings, social conditions and insecurity. Investments, savings and human development index have a positive and significant coefficient, which indicates that these variables produce incentives for economic development, whereas GINI and homicides have a negative relationship, demonstrating that these variables undermine economic development. All findings are important in the design of strategies and policies that strengthen income distribution equality, a key factor that determines growth and development through adequate government expenditures that encourage savings and investment decisions with the aim to improve welfare and the standard of living.     

Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non‐uniform intervals and no solution is currently available when time‐varying covariates are included in the model. In this paper, we propose two new estimators for dynamic panel data models when data are irregularly spaced and compare their finite‐sample performance to the näive application of existing estimators. We illustrate the practical importance of this issue in an application concerning early childhood development. Copyright © 2016 John Wiley & Sons, Ltd.     

Neglected dynamics in panel data models; consequences and detection in finite samples*

The bias of various estimators for static cross-section and panel data models is assessed in a simulation study, where the actual data generating process is a dynamic adjustment mechanism with random individual effects. It is concluded that the consequences of incorrectly estimating a static model can be rather serious. Therefore, it is important to have an accurate technique available for the detection of dynamics. Two exact similar tests for the presence of a lagged dependent variable in panel data models are developed; in some simulation experiments these tests outperform standard asymptotic test procedures. Empirical results on Engle curves for food illustrate the above issues.     

Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model

This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects.     

Bias in dynamic panel estimation with fixed effects,incidental trends and cross section dependence

Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N→∞$N\to \infty$. The results extend earlier work by Nickell [1981. Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426] and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic trends, predetermined and exogenous regressors, and errors that may be cross sectionally dependent. The asymptotic bias is found to be so large when incidental linear trends are fitted and the time series sample size is small that it changes the sign of the autoregressive coefficient. Another finding of interest is that, when there is cross section error dependence, the probability limit of the dynamic panel regression estimator is a random variable rather than a constant, which helps to explain the substantial variability observed in dynamic panel estimates when there is cross section dependence even in situations where N is very large. Some proposals for bias correction are suggested and finite sample performance is analyzed in simulations.     

Semiparametric efficient estimation of dynamic panel data models

This paper extends the semiparametric efficient treatment of panel data models pursued by Park and Simar [Park, B.U., Simar, L., 1994. Efficient semiparametric estimation in stochastic frontier models. Journal of the American Statistical Association 89, 929–936] and Park et al. [Park, B.U., Sickles, R.C., Simar, L., 1998. Stochastic frontiers: a semiparametric approach. Journal of Econometrics 84, 273–301; Park, B.U., Sickles, R.C., Simar, L., 2003. Semiparametric efficient estimation of AR(1) panel data models. Journal of Econometrics 117, 279–309] to a dynamic panel setting. We develop a semiparametric efficient estimator under minimal assumptions when the panel model contains a lagged dependent variable. We apply this new estimator to analyze the structure of demand between city pairs for selected U.S. airlines during the period 1979 I–1992 IV.     

Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

Nonresponse in dynamic panel data models

To verify whether data are missing at random (MAR) we need to observe the missing data. There are only two exceptions: when the relationship between the probability of responding and the missing variables is either imposed by introducing untestable assumptions or recovered using additional data sources. In this paper, we briefly review the estimation and test procedures for selectivity in panel data. Furthermore, by extending the MAR definition from a static setting to the case of dynamic panel data models, we prove that some tests for selectivity are not verifying the MAR condition.     

Individual effects and dynamics in count data models

In this paper we examine the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables. Use of a linear feedback model is proposed. A quasi-differenced GMM estimator is consistent for the parameters in the dynamic model, but when series are highly persistent, there is a problem of weak instrument bias. An estimator is proposed that utilises pre-sample information of the dependent count variable, which is shown in Monte Carlo simulations to possess desirable small sample properties. The models and estimators are applied to data on US patents and R&D expenditure.