非参数可加ACD模型及其应用研究

非参数可加ACD模型对条件期望的函数形式与随机误差项的分布形式要求都没有参数ACD模型强，因此不会像参数ACD模型那样因模型形式设定错误而得出错误结论。非参数可加ACD模型估计出来的各个可加部分图形的形状对于正确设定参数ACD模型具有一定的指导作用。     

非参数ACD模型及其在中国股票市场的实证研究

文章利用中国证券市场的日内交易数据实证了非参数ACD模型。非参数ACD模型不依赖条件均值的函数形式和误差项的分布形式,更具有一般意义。文章从多个方面进行实证分析。利用非参数方法进行分析的结果表明:数据不能用线性ACD模型来刻画,根据非参数拟合曲面的形状可以把此ACD模型的函数形式设定为某种非线性形式。     

IV估计框架下模型设定检验问题的讨论

 IV估计框架下各种统计量的良好性质依赖于相应的模型设定,如果这些模型设定未能得到数据的支持,其统计推断结论将是不可靠的。如判定计量经济模型是否存在内生性的Hausman检验,实证研究中同一问题的检验结果可能大相径庭。如何通过合理的模型设定检验程序来获得模型参数科学、可靠的估计结果和检验结论呢？本文讨论了工具变量估计框架下的各种模型设定检验问题,明确了各个检验统计量的适用条件及其逻辑联系,给出了工具变量估计框架下模型设定检验的一般步骤。     

二元选择面板模型的设定检验

 本文将RESET检验扩展到二元选择面板数据模型的设定,考察了固定效应Probit模型和Logit模型的设定检验,包括异方差、遗漏变量和分布误设的检验。模拟结果表明Logit模型的RESET设定检验显示良好的水平和功效,而Probit模型的RESET检验可能由于估计方法的选择导致在某些方面的功效表现不好。但总体说来,在二元选择面板数据模型的设定检验上,RESET检验仍然是一个较好的选择。     

一类计量经济学模型设定偏误诊断思路及展示

在分析计量经济学模型的几类基础统计检验功能的基础上,文章讨论并研究了这些检验方法用于诊断计量经济学模型设定偏误的机理及可行性,并进一步结合实际经济问题的研究过程,展示在模型构建过程中统计检验方法关于模型设定偏误诊断的作用与功能。理论分析和实证研究结果均显示,这些统计检验方法可以有效用于计量经济学模型设定偏误的诊断。     

ARMA模型的频谱分析检验法

文章将窗谱估计的技术应用于时间序列模型的检验,以解决模型构建的正确性和参数估计的准确性。通过实例验证比较的方法进行研究,对同一时间序列模型分别利用混合的拟合不足检验与本文所提的频谱分析模型检验法对其进行检验,结果显示,即使模型在5%的置信水平下通过了Q统计量检验,但模型的设定仍然有可能存在错误,无法通过频谱分析模型检验。表明频谱分析模型检验法相对于时间序列模型构建中广泛使用的Q统计量检验法,在时间序列模型设定的正确性检验方面具有更强的模型检验能力。     

计量经济学中函数设定形式的检验

在建模的实践中,并不存在惟一的方法来表现被解释变量和解释变量之间依存关系,因此在实际建模过程中,就存在一个从多个备择模型形式中进行选择以及对所选模型形式进行诊断检验的问题.函数设定形式的检验是指关于对条件期望函数(条件均值方程)的具体函数形式的设定以及对所设定的函数形式进行检验两个方面.文章对此进行了讨论.     

检验式设定错误对时间序列单位根检验小样本性质的影响

首先对单位根检验的两类常见的数据生成系统进行比较，然后利用蒙特卡洛实验研究了时间序列单位根检验式的设定问题。研究发现在利用DF检验和DF-GLS检验进行时间序列的单位根检验时，检验式设定错误直接影响着检验结果，尤其在推断时间序列是趋势平稳过程还是有时间趋势项的随机游走过程或有二阶时间趋势多项式的随机游走过程时，检验式的错误设定很容易将趋势平稳过程误判为非平稳过程。     

交易持续期、波动率与市场微观结构关系的统计检验

基于招商银行超高频数据,文章检验得出交易持续期日内效应呈现倒V形状,并以ACD模型和UHF-GARCH模型分析交易持续期变化规律以及其对收益率和波动率的影响,研究表明股票的交易持续期存在集聚效应,并且交易持续期越长,收益率越高,分析表明较长的交易持续期是由于市场参与者缺乏有效信息,知情交易者较少导致,为我国证券市场微观制度改革提供启示。     

空间面板数据模型设定问题分析

空间面板数据模型将空间计量经济学和面板数据方法相结合,不仅同时考虑时空特征,而且将空间效应纳入研究体系,成为当前计量经济学的热点研究领域,但其模型设定、参数估计及模型检验也更为复杂,实证研究中往往出现模型设定偏误等问题。因此,基于空间面板数据模型的前沿理论,重点探讨模型设定中的常见问题,包括空间滞后模型与空间误差模型的选择、随机效应与固定效应的选择以及模型拟合优度的选择与比较,为模型的应用和新模型的扩展提供理论依据和参考。     

具有GARCH（1，1）-正态误差项的非对称单位根检验研究

摘　　要:理论研究表明许多经济变量呈现出非对称的门限自回归（TAR）或动态门限自回归（M-TAR）数据生成机制,因而非对称单位根检验就成为该领域的主要研究方向之一。本文对非对称单位根检验Enders-Granger方法在GARCH（1,1）-正态误差项下的检验水平与检验势作了系统的仿真研究。研究表明：GARCH（1,1）-正态误差项的TAR或M-TAR模型会对该方法的检验水平和检验势产生重要影响。     

A note on the nonparametric test based on theL 1-version of the Cramér-von Mises statistic

We consider the test based on theL ₁-version of the Cramér-von Mises statistic for the nonparametric two-sample problem. Some quantiles of the exact distribution under H₀ of the test statistic are computed for small sample sizes. We compare the test in terms of power against general alternatives to other two-sample tests, namely the Wilcoxon rank sum test, the Smirnov test and the Cramér-von Mises test in the case of unbalanced small sample sizes. The computation of the power is rather complicated when the sample sizes are unequal. Using Monte Carlo power estimates it turns out that the Smirnov test is more sensitive to non stochastically ordered alternatives than the new test. And under location-contamination alternatives the power estimates of the new test and of the competing tests are equal.     

A stationarity test on Markov chain models based on marginal distribution

A stationarity test on Markov chain models is proposed in this paper. Most of the previous test procedures for the Markov chain models have been done based on the conditional probabilities of a transition matrix. The likelihood ratio and Pearson type chi-square tests have been used for testing stationarity and order of Markov chains. This paper uses the efficient score test, an extension of the test developed by Tsiatis (1980) [18], for testing the stationarity of Markov chain models based on the marginal distribution as obtained by Azzalini (1994) [2]. For testing the suitability of the proposed method, a numerical example of real life data and simulation studies for comparison with an alternative test procedure are given.     

Accent on Teaching Materials

A linear combination test for combining several tests of the correlation coefficient in the bivariate normal distribution is proposed. The linear combination test is compared with the well-known Fisher method of combining tests. It is shown by a Monte Carlo study that the linear combination test has a larger power.     

A simulation study on the influence of ties on uniform scores test for circular data

Uniform scores test is a rank-based method that tests the homogeneity of k-populations in circular data problems. The influence of ties on the uniform scores test has been emphasized by several authors in several articles and books. Moreover, it is suggested that the uniform scores test should be used with caution if ties are present in the data. This paper investigates the influence of ties on the uniform scores test by computing the power of the test using average, randomization, permutation, minimum, and maximum methods to break ties. Monte Carlo simulation is performed to compute the power of the test under several scenarios such as having 5% or 10% of ties and tie group structures in the data. The simulation study shows no significant difference among the methods under the existence of ties but the test loses its power when there are many ties or complicated group structures. Thus, randomization or average methods are equally powerful to break ties when applying uniform scores test. Also, it can be concluded that k-sample uniform scores test can be used safely without sacrificing the power if there are only less than 5% of ties or at most two groups of a few ties.     

A test for uniformity based on informational energy

In this paper a test of fit for uniformity based on the estimated Informational Energy is proposed. The test usesm-step spacings. The test is shown to be a consistent test of the null hypothesis. Percentage points and power against different alternatives are calculated. Finally, our test is compared with other test of uniformity. This work was supported by the grant DGES BMF2000-0800.     

Multivaritate two stage test based on ranks

A distribution free two stage test based on ranks for the multivariate two sample location problem is presented. The asymptotic distribution of the first and second stage test statistics is derived. Results of a Monte Carlo power study are used to compare the two stage test with the usual one stage test. A brief table of critical values is also presented. The test is illustrated by using data from an exercise study conducted by the Multipurpose Arthritis center.     

基于辅助回归模型的空间Hausman检验

 基于面板数据空间误差分量模型,提出空间Hausman检验,并通过数理推导,构造辅助回归模型的空间Hausman检验,进而通过Monte Carlo模拟实验,研究空间Hausman检验,以及辅助回归空间Hausman检验的有限样本性质。研究结果表明,空间Hausman检验能有效矫正空间面板数据下经典Hausman检验的水平扭曲,但随着空间相关性和样本量增大,其水平扭曲偏离理想值;辅助回归空间Hausman检验始终保持理想的水平扭曲。此外,二者均具有优越的检验功效。     

Multiple hypothesis test for parameter constancy based on recursive residuals

This article presents a multiple hypothesis test procedure that combines two well known tests for structural change in the linear regression model, the CUSUM test and the recursive t test. The CUSUM test is run through the sequence of recursive residuals as usual; if the CUSUM plot does not violate the critical lines, one more step is taken to perform the t test for hypothesis of zero mean based on all recursive residuals. The asymptotic size of this multiple hypothesis test is derived; power simulation results suggest that it outperforms the traditional CUSUM test and complements other tests that are currently stressed in econometrics.     

The effect of spillover on the Granger causality test

In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, that is, causality in variance. The Wald test and the WW test (the Wald test with White's proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data-generating processes are used. The results show that the Wald test over-rejects the null hypothesis both with and without the spillover effect, and that the over-rejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is a spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.