两个部分线性模型的比较

内容提要:对于两个部分线性模型参数部分中模型系数是否相等的检验问题,本文基于比较原假设与备择假设下模型拟合的残差平方和的思想构造了检验统计量,并给出了计算检验_^p* 值的F分布逼近法。     

样本参数的优化检验方法

文章讨论了一种新的抽样方法,并基于这一抽样方法提出了样本参数的优化检验统计量;证明了在一定条件下,当原假设成立时,该检验统计量与简单随机抽样下参数的似然比统计量具有相同的极限分布;并进一步比较了该检验与随机抽样下的似然比检验在参数空间上的功效.从检验所犯两类错误的角度说明了基于排序集抽样的似然比检验的优良性.     

含方程误差的重复测量误差模型参数的假设检验

含方程误差的重复测量误差模型解决了协变量真值与响应变量真值之间存在的不完全匹配问题.为使中小型样本量下的假设检验结果更为准确,文章基于多元正态分布推导改进形式的Skovgaard似然比检验统计量,提高其在原假设下收敛到卡方分布的渐近速度,并应用该检验统计量对重复测量误差模型中回归参数的显著性进行假设检验.模拟研究的结果表明改进的似然比检验统计量在有限样本检验下的优越性;实例分析中通过检验气温与气压之间回归参数的显著性来说明该方法的实用性.     

混合地理加权回归模型的统计诊断

混合地理加权回归模型作为一类能简单有效解决空间非平稳问题的数据分析方法已经得到了广泛的应用.在利用该模型分析实际数据时,一个或多个特殊观测点的存在能导致估计结果发生较大改变.为了能有效检测出异常点,系统研究这类半参数模型的统计诊断与影响分析.首先基于数据删除模型定义了参数分量对应的Cook统计量,其次,基于均值漂移模型讨论了异常点的检验问题,构造了相应的检验统计量.     

协整向量误设对相关检验的影响：小样本模拟研究

利用蒙特卡罗模拟方法,研究小样本中协整向量误设对两种t统计量(tDF和tECM)的实际检验水平和检验势的影响,并针对参数q′的两种取值分别对二者的检验势进行比较,认为向量误设会降低tDF和tECM的检验势,但如果误设程度不大,其具体表现依然优于相应的无约束情形.无论是否存在向量误设,统计量tECM都优于tDF,但其优势会随着q′的减小而逐渐丧失.     

季节异方差序列的季节调整问题研究

季节调整是从经济序列中剔除季节成分的重要方法。季节异方差的存在,使经典的季节调整方法无法彻底分离出季节成分,致使季节调整失败。本文针对季节异方差问题提出用于季节调整的改进的HS模型,并定义改进的HS模型构造季节异方差检验LR统计量,通过蒙特卡洛模拟方法分析该检验的检验尺度和检验功效。最后,利用我国税收总额月度序列给出实证分析,并通过对比考察了改进的HS模型方法季节调整的有效性。     

STAR模型框架下的wild bootstrap单位根检验

考虑随机误差项存在异方差的情形,文章建立了STAR模型框架下的wild bootstrap单位根检验策略.Monte Carlo模拟研究的结果表明,若时间序列存在GARCH异方差,KSS非线性单位根检验统计量的检验水平扭曲程度要远高于线性ADF统计量,且GARCH特征越明显,扭曲程度越高.无论GARCH特征明显与否,wild bootstrap单位根检验方法都不存在检验水平扭曲,且具有理想的检验势.     

响应变量缺失下半参数变系数EV模型的约束统计推断

文章研究了半参数变系数EV模型在线性约束条件下的估计和检验问题,当响应变量缺失、非参数部分协变量带有测量误差时,利用局部纠偏的Profile最小二乘估计、Lagrange乘子方法和借补技术构造了回归模型参数分量两类纠偏约束估计量。此外,为了检验线性约束条件,构造了借补的Profile Lagrange乘子检验统计量,并通过蒙特卡洛数值模拟验证估计量和检验统计量的有效性。     

可加模型的序列相关检验

可加模型是一类应用广泛的半参数模型,为了检验模型误差是否存在有限阶数的序列相关,基于由Backfitting估计方法得到残差构造了检验统计量,并证明了该统计量的渐近零分布为正态分布或卡方分布,最后通过模拟试验验证了该检验方法的有效性。     

LSTAR框架下一类针对单位根原假设的F检验

在LSTAR框架下构建了检验单位根原假设的F类型统计量,并推导了其极限分布。相较于之前学者的研究,对LSTAR模型线性系数和位置参数的约束得以放松,因此更具有普适性;有限样本下的仿真模拟表明,相比较ADF统计量以及刘雪燕等(2008)提出的t统计量,F统计量在LSTAR框架下具有更大的检验势。对人民币实际汇率的PPP检验进一步印证了F检验在相关应用研究中的适用性和优越性。     

A Lagrange multiplier test for testing the adequacy of constant conditional correlation GARCH model

ABSTRACT

A Lagrange multiplier test for testing the parametric structure of a constant conditional correlation-generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which represents the null model, whereas the other one describes the misspecification. A simulation study shows that the test has good finite sample properties. We compare the test with other tests for misspecification of multivariate GARCH models. The test has high power against alternatives where the misspecification is in the GARCH parameters and is superior to other tests. The test is not greatly affected by misspecification in the conditional correlations and is therefore well suited for considering misspecification of GARCH equations.     

C338. Misspecified location and scale. 111. the use of S2

The observed high kurtosis of stock market returns and other variables of a speculative nature has aroused interest in stable law distributions. This paper makes the point that most historical findings that returns indeed follow stable laws may have been caused by conditional heteroskedasticity. This presumption is enhanced by Monte Carlo simulations.     

The effect of conditional heteroskedasticity on common statistical procedures for means and variances

Summary: Commonly used standard statistical procedures for means and variances (such as the t–test for means or the F–test for variances and related confidence procedures) require observations from independent and identically normally distributed variables. These procedures are often routinely applied to financial data, such as asset or currency returns, which do not share these properties. Instead, they are nonnormal and show conditional heteroskedasticity, hence they are dependent. We investigate the effect of conditional heteroskedasticity (as modelled by GARCH(1,1)) on the level of these tests and the coverage probability of the related confidence procedures. It can be seen that conditional heteroskedasticity has no effect on procedures for means (at least in large samples). There is, however, a strong effect of conditional heteroskedasticity on procedures for variances. These procedures should therefore not be used if conditional heteroskedasticity is prevalent in the data.*We are grateful to the referees for their useful and constructive comments.     

A non-linear time series approach to modelling asymmetry in stock market indexes

In this paper we analyse the performances of a novel approach to modelling non-linear conditionally heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is based on the combination of a TAR model for the conditional mean with a Constrained Changing Parameters Volatility (CPV-C) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ composite and FTSE 100 stock market indexes.     

Factor Stochastic Volatility in Mean Models: A GMM Approach

This paper provides a semiparametric framework for modeling multivariate conditional heteroskedasticity. We put forward latent stochastic volatility (SV) factors as capturing the commonality in the joint conditional variance matrix of asset returns. This approach is in line with common features as studied by Engle and Kozicki (1993), and it allows us to focus on identication of factors and factor loadings through first- and second-order conditional moments only. We assume that the time-varying part of risk premiums is based on constant prices of factor risks, and we consider a factor SV in mean model. Additional specification of both expectations and volatility of future volatility of factors provides conditional moment restrictions, through which the parameters of the model are all identied. These conditional moment restrictions pave the way for instrumental variables estimation and GMM inference.     

The coefficient of variation asymptotic distribution in the case of non-iid random variables

Due to the widespread use of the coefficient of variation in empirical finance, we derive its asymptotic sampling distribution in the case of non-iid random variables to deal with autocorrelation and/or conditional heteroskedasticity stylized facts of financial returns. We also propose statistical tests for the comparison of two coefficients of variation based on asymptotic normality and studentized time-series bootstrap. In an illustrative example, we analyze the monthly return volatility of six stock market indexes during the years 1990–2007.     

Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

ABSTRACT

For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.     

一个新的稳健ARCH检验和YJ-GARCH模型

 众所周知,Engle (1982) 的ARCH检验对于条件均值模型误设并不稳健,特别地,当条件均值是非线性过程而我们仅对之建立线性模型时,它过度地拒绝真实的原假设,导致出现严重的水平扭曲 (size distortion)。因此,本文在文献当中首次利用Yeo-Johnson变换方法来转换均值模型的因变量以排除ARCH 过程中均值部分的非线性,进而提出一个新的稳健ARCH检验以及一个新的GARCH模型——Yeo-Johnson (YJ) GARCH模型。蒙特卡罗模拟结果表明,稳健的ARCH检验在水平 (size) 和势 (power) 方面的表现要显著优于Engle (1982) 的ARCH检验。对上证综指收益率的实证研究结果表明,YJ-GARCH模型的拟合效果要显著优于线性GARCH模型。     

A dynamic analysis of stock markets using a hidden Markov model

This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.     

Quasi maximum likelihood estimation of dynamic panel data models

This article establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The article also provides an ECME algorithm for calculating levels QML estimates. Finally, it compares the finite-sample performance of levels and differenced QML estimators, the differenced generalized method of moments (GMM) estimator, and the system GMM estimator. The QML estimators usually have smaller— typically substantially smaller—bias and root mean squared errors than the panel data GMM estimators.