协整向量误设对相关检验的影响

常用于检验既定协整关系的统计量有tDF和tECM两种，但由于真实数据生成过程未知，估计模型中可能存在一定程度的协整向量误设，从而使统计量的分布特征受到影响。本文首先探讨tDF检验的隐含系数约束α=γ，即短期弹性等于先验长期弹性；其次分析零假设下两种统计量的分布特征，以及先验设定γ对信号噪声比q进而对tECM分布特征的影响；最后在局部备择假设下，给出两种统计量的渐近分布，并表明向量误设会降低协整检验的势，其程度与设定误差d正相关。     

随机系数面板数据单位根联合LM检验的稳定性研究

本文从初始值的视角研究其对随机系数面板数据单位根联合LM检验稳定性的影响。推导当初始值不是依概率有界的随机变量而是渐近不可忽略的变量时联合LM检验统计量的渐近分布,并发现联合LM检验不再服从其原分布,且其与初始值有关,表明联合LM检验统计量的渐近性质不稳定。蒙特卡洛模拟结果显示,在有限样本情形下,初始值可能会导致联合LM检验统计量出现较严重的水平扭曲现象,即联合LM检验的统计性质不稳定,易受初始值影响。     

面板单位根Hadri检验的有偏性及其修正

Hadri(2000)根据单一时间序列的KPSS检验,提出了以平稳性为原假设的面板数据单位根检验。但我们的仿真试验表明,对短时间序列数据,其基于残差的拉格朗日乘数(LM)统计量是有偏的,使得在此基础之上进行的Hadri检验不再服从标准正态渐进分布。本文通过蒙特卡罗仿真对LM统计量进行了修正,修正之后的Hadri检验统计量的渐进分布为标准正态分布。仿真的结果显示,修正了LM值的Hadri检验具有更好的小样本性质和更高的检验势。     

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)。     

具有GARCH(1,1)-Normal-errors的单位根过程DF检验的可靠性研究

本文指出了Kim和Schmidt（1993）等在研究GARCH-errors对DF单位根检验有限样本性质影响时存在的方法上的缺陷。通过理论分析和Monte Carlo随机模拟，发现对于具有GARCH（1，1）-Normal-errors的单位根过程采用DF统计量进行检验应遵循以下规律：（1）对于任意给定的初始条件方差h0和条件方差方程的常数项ω，当去掉初始生成的数据足够多时，可以得到相当平稳的误差项序列，并且h0和ω对DF统计量k和r分布的影响可以忽略不计；（2）无论采用DF检验的临界值还是采用统计量的实际分位数，k检验均比τ检验具有较高的可靠性；（3）对于给定的干扰项系数，条件方差方程的系数和越高，k检验和τ检验的可靠性越差。     

自举法与协整参数的FMOLS估计

考虑静态和动态两类数据生成过程,利用蒙特卡罗模拟方法,从估计偏差、实际检验水平和检验功效三个方面对FMOLS(Full Modified Ordinary Least Square)估计的渐近分析和自举分析进行全面比较.结果表明,与渐近分析相比,自举分析可以减小实际检验水平对名义水平的偏差,但要以检验功效的降低为代价.严格意义上,自举分析是降低了"拒真"错误出现的概率.相对稳健的选择是结合两方面的信息,渐近检验通过则接受原假设,自举检验拒绝则接受备择假设,对于其他情况,具体结论取决于人们的研究态度.     

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。     

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。     

DF检验式中联合检验F统计量分布特征

单位根检验同被检验的模型中是否存在常数项、时间趋势项密切相关，针对常数项和时间趋势项及其t统计量的分布特征目前得到了很好的研究，但是相应的联合检验F统计量研究很少，而Hatanka（1996）就提到了该统计量的重要性，并认为有必要对其分布特征进行研究。本文通过蒙特卡罗模拟，分析了DF检验式中联合检验F统计量的分布特征，在此基础上给出了有限样本条件下各检验统计量的响应面函数，从而使得单位根检验进一步完善。     

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”。     

Local Asymptotic Power of Breitung's Test*

In this article, we derive the local asymptotic power function of the unit root test proposed by Breitung [Journal of Econometrics (2002) Vol. 108, pp. 343–363]. Breitung's test is a non‐parametric test and is free of nuisance parameters. We compare the local power curve of the Breitungs’ test with that of the Dickey–Fuller test. This comparison is in fact a quantification of the loss of power that one has to accept when applying a non‐parametric test.     

Point optimal tests of the null hypothesis of cointegration

This paper obtains an asymptotic Gaussian power envelope for tests of the null hypothesis of cointegration. In addition, the paper proposes a feasible point optimal cointegration test whose local asymptotic power function is found to be close to the asymptotic Gaussian power envelope.     

Design considerations for neyman-pearson and wald hypothesis testing

Summary The Neyman-Pearson Lemma describes a test for two simple hypotheses that, for a given sample size, is most powerful for its level. It is usually implemented by choosing the smallest sample size that achieves a prespecified power for a fixed level. The Lemma does not describe how to select either the level or the power of the test. In the usual Wald decision-theoretic structure there exists a sampling cost function, an initial prior over the hypothesis space and various payoffs to right/wrong hypothesis selections. The optimal Wald test is a Bayes decision rule that maximizes the expected payoff net of sampling costs. This paper shows that the Wald-optimal test and the Neyman-Pearson test can be the same and how the Neyman-Pearson test, with fixed level and power, can be viewed as a Wald test subject to restrictions on the payoff vector, cost function and prior distribution.     

A non-local perspective on the power properties of the CUSUM and CUSUM of squares tests for structural change

We consider the power properties of the CUSUM and CUSUM of squares (CUSQ) tests in the presence of a one-time change in the parameters of a linear regression model. A result due to Ploberger and Krämer [1990. The local power of the cusum and cusum of squares tests. Econometric Theory 6, 335–347.] is that the CUSQ test has only trivial asymptotic local power in this case, while the CUSUM test has non-trivial local asymptotic power unless the change is orthogonal to the mean regressor. The main theme of the paper is that such conclusions obtained from a local asymptotic framework are not reliable guides to what happens in finite samples. The approach we take is to derive expansions of the test statistics that retain terms related to the magnitude of the change under the alternative hypothesis. This enables us to analyze what happens for non-local to zero breaks. Our theoretical results are able to explain how the power function of the tests can be drastically different depending on whether one deals with a static regression with uncorrelated errors, a static regression with correlated errors, a dynamic regression with lagged dependent variables, or whether a correction for non-normality is applied in the case of the CUSQ. We discuss in which cases the tests are subject to a non-monotonic power function that goes to zero as the magnitude of the change increases, and uncover some curious properties. All theoretical results are verified to yield good guides to the finite sample power through simulation experiments. We finally highlight the practical importance of our results.     

Distribution-free tests for time series models specification

We consider a class of time series specification tests based on quadratic forms of weighted sums of residuals autocorrelations. Asymptotically distribution-free tests in the presence of estimated parameters are obtained by suitably transforming the weights, which can be optimally chosen to maximize the power function when testing in the direction of local alternatives. We discuss in detail an asymptotically optimal distribution-free alternative to the popular Box–Pierce when testing in the direction of AR or MA alternatives. The performance of the test with small samples is studied by means of a Monte Carlo experiment.     

On the comprehensive method of testing non-nested regression models

The paper derives the specific form of the exponentially combined likelihood function of two competing multivariate non-linear regression models and shows that the application of the comprehensive approach to testing non-nested regression models will, in general, be indeterminate. It establishes that in the univariate case there exists a large number of tests of non-nested regression models which are consistent in addition to having the same asymptotic distribution under the null hypothesis. The paper then derives a set of conditions under which all these consistent tests are asymptotically equivalent not only under the null hypothesis but also under local alternatives. As an application of this latter result the paper establishes the asymptotic equivalence of the tests recently proposed by Davidson and MacKinnon, and Fisher and McAleer under local alternatives, and shows that within the class of tests considered in the paper these proposed tests possess maximum local power. The latter test has this property only when the number of explanatory variables of the ‘true’ model is not more than that of the ‘false’ model.     

Robust and Powerful Tests for Nonlinear Deterministic Components

We develop a test for the presence of nonlinear deterministic components in a univariate time series, approximated using a Fourier series expansion, designed to be asymptotically robust to the order of integration of the process and to any weak dependence present. We show that our proposed test has uniformly greater local asymptotic power than the existing tests of Harvey, Leybourne and Xiao (2010) when the shocks are I(1), identical local asymptotic power when the shocks are I(0), and also improved finite sample properties. We also consider the issue of determining the number of Fourier frequencies used to specify any nonlinear deterministic components.     

A Correction for Local Biasedness of the Wald and Null Wald Tests

This paper proposes a procedure to overcome the problem of small-sample local biasedness of the Wald test of a single restriction. The new test procedure involves finding two critical values, in order to obtain the desired size and local unbiasedness. Simulation results indicate that the Wald test corrected in this way still shares the non-monotonic power behaviour of the original test reported in the literature. We extend the new test procedure to the null Wald test recently developed to resolve power non-monotonicity, and obtain good power properties.     

Power of Tests for Unit Roots in the Presence of a Linear Trend*

Dickey and Fuller [Econometrica (1981) Vol. 49, pp. 1057–1072] suggested unit‐root tests for an autoregressive model with a linear trend conditional on an initial observation. TPower of tests for unit roots in the presence of a linear trendightly different model with a random initial value in which nuisance parameters can easily be eliminated by an invariant reduction of the model. We show that invariance arguments can also be used when comparing power within a conditional model. In the context of the conditional model, the Dickey–Fuller test is shown to be more stringent than a number of unit‐root tests motivated by models with random initial value. The power of the Dickey–Fuller test can be improved by making assumptions to the initial value. The practitioner therefore has to trade‐off robustness and power, as assumptions about initial values are hard to test, but can give more power.     

Improved likelihood ratio tests for cointegration rank in the VAR model

We suggest improved tests for cointegration rank in the vector autoregressive (VAR) model and develop asymptotic distribution theory and local power results. The tests are (quasi-)likelihood ratio tests based on a Gaussian likelihood, but as usual the asymptotic results do not require normally distributed innovations. Our tests differ from existing tests in two respects. First, instead of basing our tests on the conditional (with respect to the initial observations) likelihood, we follow the recent unit root literature and base our tests on the full likelihood as in, e.g., Elliott et al. (1996). Second, our tests incorporate a “sign” restriction which generalizes the one-sided unit root test. We show that the asymptotic local power of the proposed tests dominates that of existing cointegration rank tests.