异方差问题伪检验研究北大核心CSCDCSSCI

研究目标:探讨异方差相关的伪检验及参数可分辨问题。研究方法:运用理论分析和蒙特卡洛仿真方法。研究发现:同方差或异方差情形使用错误异方差的WLS的假设检验是错误的,易导致伪回归,常比直接使用OLS更糟;异方差时虽估计是一致的,即使大样本也不可直接使用OLS进行假设检验,因得到伪回归或将显著参数误判为不显著的可能性不会随样本增大而改善;稳健方差估计可得到OLS实际方差的较好估计,对原假设误判通常无影响,但参数分辨力变差,检验功效明显下降;WLS的BLUE性很不稳健,只识别出引起异方差的变量而不了解其完整结构没有意义。研究创新:推导出参数分辨率公式,发现避免异方差伪检验的思路。研究价值:为正确评价存在异方差的模型提供了有益的建议。     

基于分组的异方差检验和两阶段估计

本文提出了一种基于分组的异方差检验法，并给出了存在异方差时的两阶段估计。     

恩格尔曲线研究中的异方差问题

异方差的来源和克服方法研究恩格尔曲线的一种主要方法是横截面分析。在拟合恩格尔曲线时,人们不是直接采用原始的调查数据,而是采用按照收入等级或人均收入分组后的家庭收支数据。实践证明,这样作的好处是:可以避免由单个家庭收支数据所产生的较大的离散度,从而提高拟合程度;同时可以减少计算时间。然而,分组数据也相应地带来了异方差问题。     

CKLS-JUMP过程驱动的利率动态模型:理论估计与实证模拟

利率期限结构动态模式研究已经成为现代金融领域的一个研究热点,而跳跃扩散过程已经成为模拟存贷款利率最为有效的动态模型。本文主要以商业银行存贷款利率为对象,研究分析利率期限结构的动态变化过程。首先基于存贷款利率的变化特征,建立利率的CKLS-JUMP跳跃扩散模型;其次,运用马尔科夫链蒙特卡罗模拟方法(MCMC)对其参数进行理论估计;最后,以我国商业银行五年期存贷款利率为例进行实证模拟。研究结论认为:CKLS-JUMP模型更加符合我国存贷款利率动态行为;同时MCMC方法比传统估计方法更加准确。     

横断面样本建模中的异方差问题和巴特列特检验

异方差问题是用横断面样本建模中的一个重要问题。我在建立上海农村横断面样本综合经济模型时,对异方差及其检验,特别是巴特列特(Bartlett)检验进行了研究,发现了一些应注意的问题。     

带未知异方差与半线性结构Tobit模型的半参数估计

本文将Tobit模型扩展至同时带未知条件异方差与半线性结构回归函数的场合,并提出一种计算简便的半参数二步估计法。该方法的关键之处在于连续两次施以成对相减变换,并先后消去第一步所得被解释变量非参数条件分位函数中的两类非线性冗余成分(非线性回归函数部分与未知异方差结构)。文章证明了估计量的n-一致性与渐近正态性,并通过Monte Carlo模拟研究了分位点对的选择、扰动项分布类型与样本删尾程度等因素对估计量小样本性质的影响。最后通过国内居民医疗服务利用不平等的实例验证了本文所提的方法。     

非参数统计视角下的异方差检验设计及其应用

以数据生成过程为导向,探讨了异方差来源的基本类型;依据非参数统计的基本思想,设计了切实可行的Mood方差检验方法与平方秩检验方法,并针对异方差来源类型,分析了相应的检验思路。蒙特卡罗模拟表明,Mood方差检验方法在异方差检验方面具有很高的检验效力;平方秩检验方法在异常经济现象情形下检验效力较低,而在其他情形下检验效力很高。同时,进一步阐释了纠正异方差的基本逻辑。     

非参数空间滞后模型的贝叶斯估计

本文采用Bayes方法对非参数空间滞后模型进行全面分析,包括参数的估计以及用自由节点样条来拟合未知联系函数。所建议的Bayes方法通过逆跳Markov chain Monte carlo算法（RJMCMC）来实现。在进行贝叶斯分析时,对样条系数与误差方差选取共轭的正态—逆伽玛先验分布,进而获得其他未知量的边际后验分布;另外,文章还设计了一个简单但一般的随机游动Metropolis抽样器,以方便从空间权重因子的条件后验分布中进行抽样。最后应用所建议的方法进行数值模拟。     

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。     

有序选择因子结构模型的MCMC估计及应用

为了评估中国分布类政策效应,根据中国微观数据的变量可得性,本文在Heckman等构建的因子结构模型基础上,将Heckman基准模型中的连续型测度方程调整为离散型有序选择模型,建立了有序选择因子结构模型,并推导出MCMC估计方法。运用该方法,结合中国样本数据,本文对高等教育的分布类政策效应进行了实证估计。有序选择因子结构模型及其MCMC估计方法对于经济政策的分布类效应评估具有普遍的理论适应性和实际应用价值。     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimator introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation.     

行业间生产率联动对中国工业生产率增长的影响——引入经济距离矩阵的空间GMM估计

本文把反映行业间生产率联动的购买距离矩阵和销售距离矩阵引入空间自回归模型,研究行业间生产率联动对我国工业生产率增长的影响。为了克服引入社会经济距离矩阵带来的异方差和矩阵的行标准化问题,本文采用空间GMM法进行模型的估计。结果表明,行业生产率联动对我国工业生产率增长具有显著的正影响,并且在资源密集型、劳动密集型和资本密集型工业行业中,行业间生产率联动对工业生产率增长的影响相对于其他因素的影响更为稳健。此外,由销售距离矩阵所体现的联动作用效果整体上大于购买距离矩阵体现的相关效果。     

The effect of estimation method and sample size in multilevel structural equation modeling

Multilevel structural equation modeling (multilevel SEM) has become an established method to analyze multilevel multivariate data. The first useful estimation method was the pseudobalanced method. This method is approximate because it assumes that all groups have the same size, and ignores unbalance when it exists. In addition, full information maximum likelihood (ML) estimation is now available, which is often combined with robust chi‐squares and standard errors to accommodate unmodeled heterogeneity (MLR). In addition, diagonally weighted least squares (DWLS) methods have become available as estimation methods. This article compares the pseudobalanced estimation method, ML(R), and two DWLS methods by simulating a multilevel factor model with unbalanced data. The simulations included different sample sizes at the individual and group levels and different intraclass correlation (ICC). The within‐group part of the model posed no problems. In the between part of the model, the different ICC sizes had no effect. There is a clear interaction effect between number of groups and estimation method. ML reaches unbiasedness fastest, then the two DWLS methods, then MLR, and then the pseudobalanced method (which needs more than 200 groups). We conclude that both ML(R) and DWLS are genuine improvements on the pseudobalanced approximation. With small sample sizes, the robust methods are not recommended.     

Nonlinear regression models with single‐index heteroscedasticity

We consider nonlinear heteroscedastic single‐index models where the mean function is a parametric nonlinear model and the variance function depends on a single‐index structure. We develop an efficient estimation method for the parameters in the mean function by using the weighted least squares estimation, and we propose a “delete‐one‐component” estimator for the single‐index in the variance function based on absolute residuals. Asymptotic results of estimators are also investigated. The estimation methods for the error distribution based on the classical empirical distribution function and an empirical likelihood method are discussed. The empirical likelihood method allows for incorporation of the assumptions on the error distribution into the estimation. Simulations illustrate the results, and a real chemical data set is analyzed to demonstrate the performance of the proposed estimators.     

Parametric links for binary choice models: A Fisherian–Bayesian colloquy

The familiar logit and probit models provide convenient settings for many binary response applications, but a larger class of link functions may be occasionally desirable. Two parametric families of link functions are investigated: the Gosset link based on the Student t latent variable model with the degrees of freedom parameter controlling the tail behavior, and the Pregibon link based on the (generalized) Tukey λ family, with two shape parameters controlling skewness and tail behavior. Both Bayesian and maximum likelihood methods for estimation and inference are explored, compared and contrasted. In applications, like the propensity score matching problem discussed below, where it is critical to have accurate estimates of the conditional probabilities, we find that misspecification of the link function can create serious bias. Bayesian point estimation via MCMC performs quite competitively with MLE methods; however nominal coverage of Bayes credible regions is somewhat more problematic.     

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients. Spatial Economic Analysis. The spatial model with space-varying coefficients proposed by Sun et al. in 2014 has proved to be useful in detecting the location effects of the impacts of covariates as well as spatial interaction in empirical analysis. However, Sun et al.’s estimator is inconsistent when heteroskedasticity is present – a circumstance that is more realistic in certain applications. In this study, we propose a kind of semi-parametric generalized method of moments (GMM) estimator that is not only heteroskedasticity robust but also takes a closed form written explicitly in terms of observed data. We derive the asymptotic distributions of our estimators. Moreover, the results of Monte Carlo experiments show that the proposed estimators perform well in finite samples.     

Multiple-dose design and bias-reducing methods for limiting dilution assays

This paper gives an overview of several (mostly recent) statistical contributions to the theory of Limiting and Serial Dilution Assays (LDA's, SDA's). A simple and useful method is presented for the setup of a design for an LDA or an SDA. This method is based on several user-supplied design parameters, consisting in the researcher's advance information and other parameters inherent to the particular problem. The commonly used Maximum Likelihood (ML) and Minimum Chi-square methods for the estimation of the unknown parameter in an LDA or an SDA are described and compared to several bias-reducing estimation methods, e.g. jackknife and bootstrap versions of the ML method. One particular jackknife version is recommended.     

LM tests of spatial dependence based on bootstrap critical values

To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM tests using Edgeworth expansions. Monte Carlo results show that when the conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample critical values than those based on asymptotics, and lead to significantly improved size and power. The methods are further demonstrated using more general spatial LM tests, in connection with local misspecification and unknown heteroskedasticity.     

Estimating house price appreciation: A comparison of methods

Several parametric and nonparametric methods have been advanced over the years for estimating house price appreciation. This paper compares five of these methods in terms of predictive accuracy, using data from Montgomery County, Pennsylvania. The methods are evaluated on the basis of the mean squared prediction error and the mean absolute prediction error. A statistic developed by Diebold and Mariano is used to determine whether differences in prediction errors are statistically significant. We use the same statistic to determine the effect of sample size on the accuracy of the predictions. In general, parametric methods of estimation produce more accurate estimates of house price appreciation than nonparametric methods. And when the mean absolute prediction error is used as the criterion of accuracy, the repeat sales method produces the most accurate estimate among the parametric methods we tested. Finally, of the five methods we tested, the accuracy of the repeat sales method is least diminished by a reduction in sample size.