半参数变系数空间误差回归模型的估计

研究目标：克服半参数变系数回归模型中误差项可能存在的空间相关性问题。研究方法：提出一类新的半参数变系数空间误差回归模型,并构造其截面似然估计。研究发现：在小样本条件下,模型估计量具有良好的表现,其精度随着样本容量的增加而提高;应用该方法分析我国资源禀赋与地方公共品供给之间的相互关系,进一步证实了模型较强的适用性。研究创新：证明了估计量的一致性与渐近正态性,并通过蒙特卡洛模拟考察了估计方法的小样本表现。研究价值：新方法对于其他结构的半/非参数空间计量模型理论研究具有推广价值,其估计技术在经济、管理等学科中具有应用价值。     

分整阶数半参数估计的有限样本性质研究

随着对经济和金融时间序列长记忆性的研究,分整阶数估计已成为当前理论研究的焦点问题。以对数周期图回归和局部Whittle方法为代表的半参数分整阶数估计方法在实践中得到广泛应用,但对这两类半参数估计方法的有限样本性质的比较则鲜有涉及,影响了在实践中对估计方法的选择。利用蒙特卡洛模拟方法,在不同数据产生的过程下,这两种半参数估计方法有限样本性质的研究结果表明：在ARFIMA(0, d, 0)过程下,LW类估计量具有较好的小样本性质;在平稳ARFIMA(1, d, 0)过程下,本文建议的QGPH估计量的有限样本性质要优于其他对数周期图估计量;在非平稳过程下,MGPH的偏差最小。     

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。     

带未知异方差广义空间模型的有效估计

空间单元大小以及其它的经济特征上的差异,常常会导致空间异方差问题。本文给出了广义空间模型异方差问题的三种不同估计方法。第一种方法是将异方差形式参数化,来克服自由度的不足,使用ML估计进行实现。而针对异方差形式未知时,分别采用了基于2SLS的迭代GMM估计和更加直接的MCMC抽样方法加以解决,特别是MCMC方法表现得更加优美。蒙特卡罗模拟表明,给定异方差形式条件下, ML估计通过异方差参数化的方法依然可以获得较好的估计效果。而异方差形式未知的情况下,另外两种方法随着样本数的增大时也可以与ML的估计结果趋于一致。     

半参数变系数空间滞后模型的截面极大似然估计

在线性参数空间滞后模型中,解释变量的系数一般假设为固定常数,本文首先放松了这种假设,将解释变量的系数设定为某一变量的未知函数,提出一类全新的半参数变系数空间滞后模型;其次导出了该模型的截面极大似然估计,并证明了该估计的一致性;最后用蒙特卡洛数值模拟方法考察了该估计在小样本条件下的性质,数值模拟结果显示我们提出的估计方法在小样本条件下依然有优良的表现。     

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。     

抽样方法的比较研究

本文通过对不同抽样方法下抽样方差计算公式的导出与比较，认为抽样中应采用何种抽样方法取决于两个参数：异方差测度r和回归截距项а，并提出了这两个参数的事前估算方法。     

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。     

自回归条件异方差模型的研究分析

自回归条件异方差（ARCH）模型适用于对具有群集性和方差时变性特点的经济类时间序列数据的回归分析和预测。本文对ARCH模型中待定参数的确定进行了详细推导；探讨了对ARCH模型扰动影响的敏感性进行分析计算的方法；并实例应用ARCH模型对股票收盘价格的全年变动进行预测，研究分析其特点。     

VaR样本分位数估计的偏差改进

本文指出了人们通常所使用的VaR样本分位数估计量会产生高估或低估的现象,并分析了产生这些现象的原因,提出在样本较大的情况下利用加权样本分位数估计量去估计VaR,在样本较小的情况下用基于Bootstrap方法的样本分位数估计量去估计VaR。数值模拟的结果表明,这些估计方法的估计精度得到了较好地改进。最后,运用这两种分位数估计量来估计两支股票(招商银行、中国石化)的日对数回报序列的VaR值,并比较它们的风险估计量的大小。     

FIML estimation of the dynamic simultaneous equations model with ARMA disturbances

A method is presented for the estimation of the parameters in the dynamic simultaneous equations model with vector autoregressive moving average disturbances. The estimation procedure is derived from the full information maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure involves only generalized instrumental variables estimation in the second step. This procedure also serves as the basis for an iterative scheme to solve the normal equations and obtain the maximum likelihood estimates of the conditional likelihood function. A nine-equation variant of the quarterly forecasting model of the US economy developed by Fair is then used as a realistic example to illustrate the estimation procedure described in the paper.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

分位数局部调整模型及应用

本文基于分位数的回归理论与方法,提出了一个新的经济计量模型:分位数局部调整模型,并给出了其数学表示、参数估计与预测方法等一整套建模技术。分位数局部调整模型能够细致地给出响应变量在各个分位点上的条件分位数,便于揭示响应变量位置、散布与形状等动态调整过程的全景信息,从而得到比均值局部调整模型更为深刻的结果。最后,将分位数局部调整模型应用于中国货币需求分析,结果显示,在货币需求的不同阶段,不仅调整速度不同,调整方式也呈现出非对称性;M1存在货币失踪之谜现象,而M2却在条件密度第一个最优区域实现了供求均衡;最优货币需求条件密度曲线较为分散,这为央行制定货币政策预留了足够的空间。     

基于极值分布理论的VaR与ES度量

本文应用极值分布理论对金融收益序列的尾部进行估计,计算收益序列的在险价值VaR和预期不足ES来度量市场风险。通过伪最大似然估计方法估计的GARCH模型对收益数据进行拟合,应用极值理论中的GPD对新息分布的尾部建模,得到了基于尾部估计产生收益序列的VaR和ES值。采用上证指数日对数收益数据为样本,得到了度量条件极值和无条件极值下VaR和ES的结果。实证研究表明:在置信水平很高(如99%)的条件下,采用极值方法度量风险值效果更好。而置信水平在95%下,其他方法和极值方法结合效果会很好。用ES度量风险能够使我们了解不利情况发生时风险的可能情况。     

Joint and marginal specification tests for conditional mean and variance models

This article proposes a class of joint and marginal spectral diagnostic tests for parametric conditional means and variances of linear and nonlinear time series models. The use of joint and marginal tests is motivated from the fact that marginal tests for the conditional variance may lead to misleading conclusions when the conditional mean is misspecified. The new tests are based on a generalized spectral approach and do not need to choose a lag order depending on the sample size or to smooth the data. Moreover, the proposed tests are robust to higher order dependence of unknown form, in particular to conditional skewness and kurtosis. It turns out that the asymptotic null distributions of the new tests depend on the data generating process. Hence, we implement the tests with the assistance of a wild bootstrap procedure. A simulation study compares the finite sample performance of the proposed and competing tests, and shows that our tests can play a valuable role in time series modeling. Finally, an application to the S&P 500 highlights the merits of our approach.     

Sequential conditional correlations: Inference and evaluation

This paper presents a new approach to the modeling of conditional correlation matrices within the multivariate GARCH framework. The procedure, which consists of breaking the matrix into the product of a sequence of matrices with desirable characteristics, in effect converts a highly dimensional and intractable optimization problem into a series of simple and feasible estimations. This in turn allows for richer parameterizations and complex functional forms for the single components. An empirical application involving the conditional second moments of 69 selected stocks from the NASDAQ100 shows how the new procedure results in strikingly accurate measures of the conditional correlations.     

固定交互效应面板截取模型的ECM算法

交互效应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,但现有研究主要集中于线性面板模型。本文将交互效应引入非线性的面板截取模型,并基于ECM算法,建立了有效估计量和识别程序。基于不同因子类型的仿真实验结果显示,ECM算法可以很好地识别面板截取样本中的非观测因子。ECM估计量具有良好的有限样本性质,与其他估计量相比具有更小的偏误和更快的收敛速度。尤其是当共同因子为低频平滑因子时,其表现最为理想。     

The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options

We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.     

No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects,jumps and i.i.d. noise: Theory and testable distributional implications

We develop a sequential procedure to test the adequacy of jump-diffusion models for return distributions. We rely on intraday data and nonparametric volatility measures, along with a new jump detection technique and appropriate conditional moment tests, for assessing the import of jumps and leverage effects. A novel robust-to-jumps approach is utilized to alleviate microstructure frictions for realized volatility estimation. Size and power of the procedure are explored through Monte Carlo methods. Our empirical findings support the jump-diffusive representation for S&P500 futures returns but reveal it is critical to account for leverage effects and jumps to maintain the underlying semi-martingale assumption.