退势单位根检验小样本性质的比较

在经验研究中，尽管Dickey—Fuller检验的DF统计量是应用最广泛的单位根检验，但是，它的检验功效偏低是众所周知的。为了改善Dickey—Fuller检验的功效，本文将时间序列的退势和DF检验、wS检验、MAX检验和RMA检验相结合，通过蒙特卡洛模拟试验研究了20种单位根检验的小样本性质，研究发现，对时间序列的退势(demeaning／detrencling)均能不同程度地改善单位根检验的功效；时间序列的递归退势RMA检验(RMA—ROLS检验)具有最理想的小样本性质，它的检验功效高于其他检验，其次是基于G15退势的DF检验(DF—G15检验)。     

单位根检验统计量MGLS的有限样本性质与应用

Ng—Perron单位根检验是值得在实际分析中广泛应用的检验方法。本文在详述M^GLS统计量的构造方法基础上,应用蒙特卡罗模拟试验分析了该统计量的有限样本分布特征,并拟合了常用显著性水平下临界值关于样本容量的响应面函数;此外,针对实际应用中检验回归式中差分变量滞后阶数的确定,本文总结了几种常用的方法,从而完善了依据M^GLS统计量进行单位根检验的程序和步骤。     

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”。     

对单位根检验PT统计量响应面函数的研究

Elliott等人于1996年提出了针对特定备择假设具有最优特性的PT单位根检验统计量。这一检验方法引起了众多计量经济学家的重视,并被收入一些新版本的计量软件包。但在实际运用时由于现有的临界值是在原文献给出的4个样本容量相应临界值的基础上插值而得,因而准确性较低,使得检验结果的可靠性受到了影响。本文利用Monte Carlo模拟的方法给出了在相当广泛的样本容量范围内临界值的近似计算公式,并对响应面函数法与插值法的结果做了一个简单的比较。     

面板协整检验有限样本性质的模拟比较

面板协整检验是基于渐近分布的检验,有限样本下统计量的检验水平和检验功效的表现涉及检验的可靠性。本文针对目前实证研究中应用最广的一类基于残差的统计量及文献中最新提出的基于准残差的统计量进行蒙特卡罗模拟,比较10个检验统计量在不同DGP设定下的检验水平和检验功效,尤其是在DGP误设时的表现。模拟结果表明:基于准残差的面板协整检验大多数情况下有着更好的检验水平和检验功效表现。这一研究为解决实证中面临的统计量可靠性甄别与选择问题提供了依据。     

依次检验策略中检验功效的Monte Carlo模拟及策略改进

依次检验策略是实施单位根检验的重要途径,但该策略并没有考虑低检验功效的影响。本文通过Monte Carlo实验,模拟了依次检验策略中的检验功效,发现在样本容量较小的情况下,该检验具有很低的检验功效。另外,本文从理论上分析了产生低功效的原因,并对该策略提出了改进。最后,采用改进后的检验策略,通过对上证综合指数序列的单位根检验验证了改进后策略的有效性。     

一种空间矩阵选取的非嵌套检验方法

本文发展了一个在空间矩阵间进行选取的非嵌套检验方法,即基于伪嵌套检验的一个变形,利用回归残差的双重检验,在备选的空间矩阵中,比较其描述空间结构的优劣。利用中国省域空间矩阵进行的Monte Carlo模拟实验发现,该方法对于中国省域空间矩阵具有一定的区分效力;而空间系数的大小、模型的设定结构及是否利用行标准化的矩阵,对于空间矩阵比较的结果具有较大的影响。     

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。     

单位根检验中的Wald统计量研究

本文研究了不同情形下单位根的Wald检验.首先从理论上推导了各种Wald检验统计量的极限分布;然后,运用蒙特卡罗试验,模拟了有限样本容量常用检验水平下的临界值,拟合了临界值关于样本容量的响应面函数,给出了检验统计量的分位数表,并总结了用于单位根检验的Wald统计量有限样本容量下的统计特性.     

Truncated Product Methods for Panel Unit Root Tests*

This paper proposes two new panel unit root tests based on Zaykin et al. (2002) ’s truncated product method. The first one assumes constant correlation between P‐values and the second one uses sieve bootstrap to allow for general forms of cross‐section dependence in the panel units. Monte Carlo simulation shows that both tests have reasonably good size and are powerful in cases of some very large P‐values. The proposed tests are applied to a panel of real GDP and inflation density forecasts, resulting in evidence that professional forecasters may not update their forecast precision in an optimal Bayesian way.     

A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test

The panel data unit root test suggested by Levin and Lin (LL) has been widely used in several applications, notably in papers on tests of the purchasing power parity hypothesis. This test is based on a very restrictive hypothesis which is rarely ever of interest in practice. The Im–Pesaran–Shin (IPS) test relaxes the restrictive assumption of the LL test. This paper argues that although the IPS test has been offered as a generalization of the LL test, it is best viewed as a test for summarizing the evidence from a number of independent tests of the sample hypothesis. This problem has a long statistical history going back to R. A. Fisher. This paper suggests the Fisher test as a panel data unit root test, compares it with the LL and IPS tests, and the Bonferroni bounds test which is valid for correlated tests. Overall, the evidence points to the Fisher test with bootstrap-based critical values as the preferred choice. We also suggest the use of the Fisher test for testing stationarity as the null and also in testing for cointegration in panel data.     

多种单位根检验法的比较研究

本文基于单位根检验基本原理,比较了5种单位根检验的方法,说明在小样本情况下,为提高检验功效,应针对数据生成过程的特点联合多种检验法进行检验。如果检验变量为非平稳,则需要进行进一步的结构突变检验,本文主张选用结构突变点内生的Perron检验法与外生检验法相结合来判断变量的平稳性。     

A Note on Seasonal Unit Root Tests

The seasonal unit root tests make it possible to determine the nature of the deterministic and stochastic seasonal fluctuations. In Section 2, we define the main seasonal time series models and the seasonal integration notion. Section 3 describes the HEGY test procedure.     

Finite Sample Correction Factors for Panel Cointegration Tests*

In this paper we present finite T mean and variance correction factors and corresponding response surface regressions for the panel cointegration tests presented in Pedroni (1999, 2004) , Westerlund (2005) , Larsson et al. (2001) and Breitung (2005) . For the single equation tests, we consider up to 12 regressors and for the system tests vector autoregression dimensions up to 12 variables. All commonly used specifications for the deterministic components are considered. The sample sizes considered are T ∈ {10,20,30,40,50,60,70,80,90,100,200,500}.     

一种结构突变面板数据单位根的联合检验

本文根据Banerjee等(1992)的模型变换方法和内生突变点选择原理,得到了一种纵剖面时间序列相互独立面板数据的内生结构突变单位根的联合检验——■_T~*(λ)检验。并且,基于泛函中心极限定理推导出了该检验统计量的渐近分布;通过蒙特卡洛模拟实验讨论了该检验的实际检验水平以及面板数据的样本大小、异质性、截距突变的幅度、斜率突变的幅度和结构突变位置等因素对检验功效的影响。最后,根据1952～2006年中国27个省份的数据讨论了人均实际收入的稳定性。     

On the Power of GLS-Type Unit Root Tests

Elliott, Rothenberg and Stock (1996), (ERS), present a 'GLS' variant of the Dickey-Fuller (DF) unit root test. Their statistic is approximately point-optimal invariant at a chosen local alternative, and usually displays better finite sample power than the DF test. Following the usual efficiency motive for GLS estimation, the higher finite sample power of the ERS test has often been attributed to the greater accuracy of the estimate of the series' non-stochastic component under stationary alternatives close to the null. This paper shows that the GLS estimates of the non-stochastic component are not, in general, more accurate. The power gain arises from the fact that the GLS statistic's null distribution has a greater positive shift relative to the DF test, than its distribution under relevant alternatives, and this persists even when the GLS estimates of the non stochastics have higher variance than the OLS estimates.