泛函中心极限定理渐进性与具有GARCH误差项的金融时序单位根检验

本文基于分析泛函中心极限定理在高频金融数据单位根检验中的特征与性质，从随机模拟的角度，探讨了有限样本情况下具有GARCH-GED误差项金融时序的ADF单位根检验统计量乙、乙的统计性质，着重研究了不同水平下的分位数、模型滞后阶的设定及GED分布参数的变动对其检验统计特性的影响。随机模拟结果显示，若数据生成模型设定为AR-GARCH-GED过程，常用的ADF单位根检验统计量存在不同的统计性质，并对出现这种现象的原因进行了探索。     

ADF单位根检验法的替代方法

研究目标：ADF单位根检验法的替代方法——消除趋势检验法。研究方法：通过蒙特卡罗仿真估算了消除趋势法、ADF法和差分法的检验临界值和检验功效;估算了三种方法检验临界值关于样本容量的响应面函数;估算了消除趋势回归中趋势项和截距项的t统计量的检验临界值及其响应面函数。研究发现：在各种情形下,消除趋势法的检验功效均大于ADF法,而且在误差项相关时更为明显,说明消除趋势法优于ADF法,可以完全替代ADF法;在某些情况下,差分法的检验功效较大。研究创新：对ADF检验模型进行了改进,提出了消除趋势检验法。研究价值：在单位根检验时,可以用消除趋势法代替一直以来被广泛采用的ADF法,提高单位根检验的功效。     

Enders-Granger方法在协整检验中的应用研究

本文将协整检验由传统的线性协整检验扩展到线性协整检验和阈值协整检验,并在Enders和Granger(1998)方法的基础上提出了一个新的检验协整是否存在的Sup-F和Sup-F*统计量。通过MC仿真研究发现:在线性协整下,ADF方法比Sup-F法具有更高的检验势,但在"持久性"较强时,Sup-F检验比ADF检验法具有更高的检验势;Sup-F统计量在Three-Regime的阈值协整检验中比ADF法有更高的检验势;Sup-F*在检验协整(包括线性协整和阈值协整)时都具有较低的检验势;随着在不同Regime中自回归系数差距的增大(非对称程度增大),sup-F统计量的检验势提高很快,且比ADF法的检验势高。     

基于协整和VECM的石油价格变动对河南省CPI影响研究

以河南省1990-2011年的石油价格与居民消费价格指数(CPI)数据为基础,运用ADF检验、协整检验和向量误差修正模型(VECM)对石油价格和居民消费价格指数的数据进行了分析和研究。研究结果表明:长期而言,石油价格与河南省居民消费价格指数之间存在单向的因果关系。石油价格每增长10%,会导致居民消费价格指数(CPI)增长0.1%;短期而言,石油价格变动对居民消费价格指数的影响存在滞后性,滞后两期比滞后一期的影响显著。这表明石油价格波动对河南省居民消费价格指数的影响程度大,影响时间久。     

含空间自回归误差项的空间动态面板模型的检验与模拟

研究目标：探究含空间自回归误差项的空间动态面板数据（SDPD）模型选择和假设检验结果的水平扭曲、检验功效。研究方法：构建含空间自回归误差项的SDPD模型的空间Hausman、LM和LR检验统计量,选择蒙特卡洛模拟进行分析。研究发现：含空间自回归误差项的SDPD模型的各类检验统计量的大样本性质良好;时空滞后项对空间Hausman检验结果的影响比空间滞后项显著;条件LMλ|α、LRλ|α检验是含空间自回归误差项随机效应SDPD模型的最优检验统计量,时空滞后项对LM、LR检验结果的影响比自回归误差项更显著。研究创新：解析了SDPD模型中各空间关联项系数的波动对模型检验精度的影响。研究价值：探究含空间自回归误差项SDPD模型的选择问题,为实证提供理论依据。     

基于残差的非对称单位根自助法检验研究

本文在研究非对称单位根检验EG法并指出其优缺点的基础上,应用基于残差的块形非参数自助法(RBB法)对EG法进行了改进,并对改进后的新方法进行了仿真研究。仿真结果表明,新的EG法不仅可以降低原EG法的检验水平扭曲,而且也具有比较高的检验势(甚至在对称单位根检验中)。同时在仿真中也发现,通过RBB法改进后的EG法可以在一定程度上克服ADF法在近单位根过程中的低势缺陷。     

国际石油价格对新疆石油工业影响的实证分析——基于VEC模型

为了研究国际油价波动对新疆石油工业的影响,本文利用2007年1月-2009年12月布伦特国际原油价格和新疆石油产业的增加值、利润、税金总额、职工平均人数各变量数据,运用ADF单位根检验对变量进行平稳性检验,得出增加值和税金总额是平稳序列,其他指标均为一阶差分平稳,说明国际石油价格与增加值和税金总额之间不存在长期均衡关系。运用VEC模型和脉冲响应函数对国际油价与利润和职工平均人数进行分析:其一,在样本期间,国际油价上涨对利润有较大的积极影响,且国际石油价格的滞后两期对利润的影响显著。其二,就长期而言,国际油价上涨对平均就业人数具有较大的负面影响;就短期而言,国际石油价格上涨对职工平均人数具有正向拉动作用,且在滞后2个月影响最大。     

同期相关面板数据退势单位根检验的小样本性质

本文基于SUR回归将时间序列的两种单位根检验（ADF—GLS检验）推广到面板数据，得到了同期相关面板数据退势单位根检验，称为SUR—ADF—GLS检验。通过蒙特卡洛试验研究发现，SUR—ADF—GLS检验具有良好的小样本性质。并且，SUR—ADF—GLS检验关于面板数据的同期相关性结构存在着较强的“依存性”。     

ADF与PP单位根检验法对非线性趋势平稳序列的伪检验

ADF与PP单位根检验对无趋势或线性趋势平稳过程可给出正确的结果。但蒙特卡罗实验表明,对非线性趋势而言,它们趋向于将平稳过程误判为有单位根。在一定条件下,各种非线性趋势可以看成准线性的,从而利用ADF与PP检验得出正确的结论。信噪比小于15倍时,PP检验可得出正确的结果;信噪比小于4倍时,ADF检验可得出正确的结果。对非线性趋势平稳序列的检验而言,PP优于ADF检验。随着干扰相对强度的增加,正确检验的可能性也大大增加。     

空间动态面板非线性Logistic模型的稳健LM检验及应用

研究目标:探究空间动态面板Logistic平滑转移自回归(SDPD-LSTAR)模型的稳健LM检验的水准和效力。研究方法:重塑得分函数和方差-协方差矩阵,推导识别时间滞后、空间滞后和非线性效应的修正稳健LM检验及其联合效应检验,通过蒙特卡洛模拟和基于STIRPAT的中国284个城市碳排放影响因素实例评估检验的功效和实践性。研究发现:稳健LM检验具有中心卡方的极限分布性质,检验功效好、计算较简便,比一般的LM检验更精确、适用性更广,该优越性随参数局部偏误的出现而显著,应用实例展现了检验良好的实践性。研究创新:提出具有时空依赖和非线性空间区制平滑转换特征的SDPD-LSTAR模型,在ML和GMM框架下推导模型的稳健LM检验。研究价值:探究SDPD-LSTAR模型的选择问题,为空间动态非线性理论和应用提供重要支持。     

Exact tests in single equation autoregressive distributed lag models

For hypotheses on the coefficient values of the lagged-dependent variables in the ARX class of dynamic regression models, test procedures are developed which yield exact inference for given (up to an unknown scale factor) distribution of the innovation errors. They include exact tests on the maximum lag length, for structural change and on the presence of (seasonal or multiple) unit roots, i.e. they cover situations where usually asymptotic and non-exact t, F, AOC, ADF or HEGY tests are employed. The various procedures are demonstrated and compared in illustrative empirical models and the approach is critically discussed.     

Unit Root Testing Using Covariates: Some Theory and Evidence

This paper analyzes the conditions under which power gains can be achieved using the Covariate Augmented Dickey-Fuller test (CADF) rather than the conventional Augmented Dickey-Fuller (ADF), and argues that this method has the advantage, relative to univariate unit root tests, of increasing power without suffering from the large size distortions affecting the latter. The inclusion of covariates affects unit root testing by: (a) reducing the standard error of the estimate of the autoregressive parameter without affecting the estimate itself, and/or (b) reducing both the standard error and the absolute value of the estimate itself. Conditions in terms of contemporaneous correlation and Granger causality are derived for case (a) or (b) to arise. As an illustration, it is shown that applying the more powerful CADF (rather than the ADF) test reverses the finding of a unit root for many US macroeconomic series.     

基于指数平滑转移模型的价格泡沫检验方法

蒙特卡洛分析显示,Phillips,Wu和Yu（2011）提出的sup ADF泡沫检验方法对扰动项的异方差较为敏感,尤其是当扰动项方差接近非平稳时存在严重的尺度扭曲,倾向于过度拒绝不存在泡沫的原假设。同时,对于Evans（1991）周期性破灭的泡沫,当泡沫破灭的概率增加时,sup ADF检验的检验势下降较快。本文结合Kapetanios,Shin和Snell（2003）单位根检验的思想,在指数平滑转移模型的框架下提出了一种新的泡沫检验方法（sup KSS检验）。与sup ADF检验相比,sup KSS检验对于扰动项的异方差有一定的改进,同时对于周期性破灭的泡沫和指数平滑转移泡沫具有较稳健的检验势。     

A Note on the Selection of Time Series Models

We consider issues related to the order of an autoregression selected using information criteria. We study the sensitivity of the estimated order to (i) whether the effective number of observations is held fixed when estimating models of different order, (ii) whether the estimate of the variance is adjusted for degrees of freedom, and (iii) how the penalty for overfitting is defined in relation to the total sample size. Simulations show that the lag length selected by both the Akaike and the Schwarz information criteria are sensitive to these parameters in finite samples. The methods that give the most precise estimates are those that hold the effective sample size fixed across models to be compared. Theoretical considerations reveal that this is indeed necessary for valid model comparisons. Guides to robust model selection are provided.     

On the cointegration of international stock indices

This paper investigates the cointegration relationship among a group of international stock indices in light of new developments of econometric methods. Kasa (1992) first documented strong evidence for cointegration relations among five national stock indices, which suggests that there exists a common trend among those stock indices. Using Johansen multivariate cointegration test, we find that his findings are persistent in a sample of longer periods and more countries. In order to investigate whether these results are driven by statistical biases related to the sample size, we apply to our tests the Johansen’s small sample correction factor. The results still point toward the existence of a cointegration relationship but the evidence becomes much weaker. We next examine the empirical patterns emerged from different lag specifications and argue that Kasa’s findings are more likely due to the size distortion in extreme long lag VAR models. Indeed, when we employ a newly developed non-parametric test that does not require estimation VAR models, the null hypothesis of no cointegration cannot be rejected for the original sample of Kasa’s five-country stock indices from 1974 to 1990, nor for the extended period from 1970 to 2003.     

Specification Sensitivity in Right‐Tailed Unit Root Testing for Explosive Behaviour

This article aims to provide some empirical guidelines for the practical implementation of right‐tailed unit root tests, focusing on the recursive right‐tailed ADF test of Phillips et al. (2011b). We analyze and compare the limit theory of the recursive test under different hypotheses and model specifications. The size and power properties of the test under various scenarios are examined and some recommendations for empirical practice are given. Some new results on the consistent estimation of localizing drift exponents are obtained, which are useful in assessing model specification. Empirical applications to stock markets illustrate these specification issues and reveal their practical importance in testing.     

The robustness of maximum likelihood and distribution-free estimators to non-normality in confirmatory factor analysis

The present Monte Carlo compares the estimates produced by maximum likelihood (ML) and asymptotically distribution-free (ADF) methods. The study extends prior research by investigating the combined effects of sample size, magnitude of correlation among observed indicators, number of indicators, magnitude of skewness and kurtosis, and proportion of indicators with non-normal distributions. Results indicate that both ML and ADF showed little bias in estimates of factor loadings under all conditions studied. As the number of indicators in the model increased, ADF produced greater negative bias in estimates of uniquenesses than ML. In addition, the bias in standard errors for both ML and ADF estimation increased in models with more indicators, and this effect was more pronounced for ADF than ML. Increases in skewness and kurtosis resulted in greater underestimating of standard errors; ML standard errors showed greater bias than ADF under conditions of non-normality, and ML chi-square statistics were also inflated. However, when only half the indicators departed from normality, the inflation in ML chi-square decreased.     

企业实践卓越绩效评价准则的探讨

本文通过企业深化推进GB/T 19580-2012《卓越绩效评价准则》的内外部需求、具体推进方法和企业贯标实例,探讨了企业为追求卓越、提升市场竞争力和取得持续的成功,导入和践行该标准的必要性.     

Testing Stationarity in Small‐ and Medium‐Sized Samples when Disturbances are Serially Correlated*

In this article, we study the size distortions of the KPSS test for stationarity when serial correlation is present and samples are small‐ and medium‐sized. It is argued that two distinct sources of the size distortions can be identified. The first source is the finite‐sample distribution of the long‐run variance estimator used in the KPSS test, while the second source of the size distortions is the serial correlation not captured by the long‐run variance estimator because of a too narrow choice of truncation lag parameter. When the relative importance of the two sources is studied, it is found that the size of the KPSS test can be reasonably well controlled if the finite‐sample distribution of the KPSS test statistic, conditional on the time‐series dimension and the truncation lag parameter, is used. Hence, finite‐sample critical values, which can be applied to reduce the size distortions of the KPSS test, are supplied. When the power of the test is studied, it is found that the price paid for the increased size control is a lower raw power against a non‐stationary alternative hypothesis.     

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.