平滑转移误差修正模型的转移函数选取问题研究

针对非线性平滑转移误差修正模型转移函数选取中存在的统计量极限分布非标准、检验统计量功效较低的问题,本文在推导非线性平滑转移协整检验统计量极限分布的基础上构造了如下转移函数选取步骤。首先,计算F_NST统计量,进行非线性平滑转移协整检验;其次,计算t_EST和t_LST统计量及相依概率P_est和P_lst;最后,比较P_est和P_lst大小并与临界值相比,得出结论。蒙特卡洛仿真模拟结果显示,转移函数选取中各统计量具有良好的功效和势,且转移函数选取中各统计量的功效明显优于其他统计量的功效。实证分析表明我国利率期限结构具有明显的非线性对称调整效应,非线性平滑转移误差修正模型中转移函数应该选取指数函数。     

协整向量、调节参数同为非线性的阈值协整检验

针对经济变量的长期均衡和短期调节关系可能同时存在非线性的事实,本文扩展现有阈值协整模型,提出了协整向量、调节参数都为非线性的阈值协整模型,并着重探讨了该模型的检验方法。研究表明,在协整关系的检验中,Wald统计量有较好的有限样本性质。在协整关系的非线性检验中,LMW和LMG统计量的水平扭曲和检验势都较好。在调节参数的非线性检验中,当调节参数具有显著的非线性时,LMH统计量表现出较好的有限样本性质。     

面板数据非线性阈值协整的检验方法

非线性阈值协整是线性协整的后续发展。本文使用两机制TR模型对Westerlund和Edgerton(2005)的面板数据协整向量结构突变模型进行扩展,提出截距项具有阈值效应、截距项和斜率系数都具有阈值效应的面板数据非线性阈值协整模型。在此基础上,本文进而分别构造Zc、Ztc、Zr、Ztr统计量检验阈值协整,并对上述统计量的极限分布进行了数学推导,发现它们都收敛于随机泛函。仿真实验结果表明,有限样本下上述检验统计量具有较小的水平扭曲和较高的检验势。     

我国国债市场利率期限结构预期假说研究

本文采用时间序列对NS模型进行研究,并在此基础上用NS模型检验了中国债券市场的纯预期假说,发现该假说在中国国债市场上具有很高的显著性,这种纯预期假说可以实现对NS模型参数的预测,进而能对利率期限结构进行预测。     

STAR误差修正模型中协整关系检验的inf-t统计量

在将误差修正过程设定为全局平稳的指数平滑转换函数的情形下,本文建立了一个新的检验统计量 对非线性STAR误差修正模型中的协整关系进行检验;推导了 统计量的渐近分布,并通过Monte Carlo模拟的方式给出了其渐近临界值。 统计量取未识别参数空间上的下确界,有效避免了原假设下的未识别参数问题。Monte Carlo 数值模拟研究的结果表明, 统计量相对E-G两步法的 和Kapetanios等（2006）的 统计量具有更高的检验势。将 统计量应用于对我国货币需求稳定性进行检验,发现我国狭义货币需求量长期稳定,短期存在指数平滑非线性机制转换特征。     

结构变点、时变期限溢价与预期假说——来自国内银行同业拆借利率的证据

在考虑时变期限溢价的基础上,本文采用结构变点方法,对中国银行同业拆借利率进行预期假说检验,并分析宏观经济因素对预期假说检验的影响。经验结果显示,1996年1月至2011年4月,银行同业拆借利率在不同区间存在共同的趋势性,长短期利差对未来短期利率变动的预测存在多个结构变点;不同区间内预期假说基本上被拒绝,时变期限溢价对预期假说检验的影响不大,而宏观因素在不同区间的影响不同。     

非线性误差校正模型中的阈值协整检验——基于阈值协整向量未知的扩展

Kapetanios等(2006)假定阈值协整向量已知,在误差校正模型中使用指数函数刻画非线性调节效应,并使用F懈统计量检验非线性阈值协整.本文基于Kapetanios等(2006)的模型设定,将阈值协整向量由已知扩展为未知,并借鉴Hansen和Seo(2002)的方法估计阈值协整向量和构造F*NEC统计量检验非线性阈值协整.仿真试验表明:本文方法估计的阈值协整向量具有近似无偏、对称的分布和相对较高的精度,并且其随样本容量的变化特征符合一致性.进一步,在有限样本下,F*NEC 与FNEC的水平扭曲没有显著差异,但F*NEC的检验势高于FNEC.     

我国银行间同业拆借利率期限结构实证研究

由于利率在市场经济中的基准作用,所以对利率期结构期限的研究一直以来都是金融领域的基础问题.上海银行间同业拆借利率(SHIBOR)是我国目前唯一的市场化利率,通过向量自回归模型(VAR)、Johansen协整检验和向量误差修正模型(VEC),对上海银行间同业拆借利率进行实证检验,建立了描述其内在动态变化规律和波动趋势的利率期限结构模型.结果表明该模型无论进行短期预测还是长期预测,都具有较高的精度,可以为各商业银行和金融机构及时调整资产负债结构、对利率衍生产品定价和正确控制金融风险提供参考依据.     

关于机理变化型变结构非线性协整的研究

变结构非线性协整是协整理论发展的必然的趋势,也是经济系统复杂多变的必然需求,文章补充了变结构非线性协整的定义,并提出了机理变化型变结构非线性协整,指出其本质问题即单位根的结构突变检验,总结了几种结构突变的单位根检验方法,讨论了变结构点的估计方法,给出了基于Chow统计量的变结构协整检验和建模方法。     

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。     

Testing cointegration relationship in a semiparametric varying coefficient model

In this paper, we develop two cointegration tests for two varying coefficient cointegration regression models, respectively. Our test statistics are residual based. We derive the asymptotic distributions of test statistics under the null hypothesis of cointegration and show that they are consistent against the alternative hypotheses. We also propose a wild bootstrap procedure companioned with the continuous moving block bootstrap method proposed in  Paparoditis and Politis (2001) and  Phillips (2010) to rectify severe distortions found in simulations when the sample size is small. We apply the proposed test statistic to examine the purchasing power parity (PPP) hypothesis between the US and Canada. In contrast to the existing results from linear cointegration tests, our varying coefficient cointegration test does not reject that PPP holds between the US and Canada.     

稳健的HAC法在多元平稳时间序列之间伪回归中的应用

本文在传统HAC法的基础上,将截断参数M设定为样本容量T,并推导新的模型显著性检验Wald*统计量的极限分布。通过比较分析,表明Wald*统计量能大大减少伪回归概率,且新统计量比传统检验统计量更加稳健,但是也发现新的统计量具有一定程度的检验水平扭曲,原因在于截断参数M的设定忽略了AR过程的持久性、MA过程的滞后阶等因素,从而导致Wald*存在检验水平扭曲,说明M的设定不当会产生伪回归和检验水平扭曲现象。     

A Wald test for the cointegration rank in nonstationary fractional systems

This paper develops new methods for determining the cointegration rank in a nonstationary fractionally integrated system, extending univariate optimal methods for testing the degree of integration. We propose a simple Wald test based on the singular value decomposition of the unrestricted estimate of the long run multiplier matrix. When the “strength” of the cointegrating relationship is less than 1/2, the test statistic has a standard asymptotic distribution, like Lagrange Multiplier tests exploiting local properties. We consider the behavior of our test under estimation of short run parameters and local alternatives. We compare our procedure with other cointegration tests based on different principles and find that the new method has better properties in a range of situations by using information on the alternative obtained through a preliminary estimate of the cointegration strength.     

Relative Efficiency of the Fuzzy p-Value Approach to Hypothesis Testing

In missing data problems, it is often the case that there is a natural test statistic for testing a statistical hypothesis had all the data been observed. A fuzzy  p -value approach to hypothesis testing has recently been proposed which is implemented by imputing the missing values in the "complete data" test statistic by values simulated from the conditional null distribution given the observed data. We argue that imputing data in this way will inevitably lead to loss in power. For the case of scalar parameter, we show that the asymptotic efficiency of the score test based on the imputed "complete data" relative to the score test based on the observed data is given by the ratio of the observed data information to the complete data information. Three examples involving probit regression, normal random effects model, and unidentified paired data are used for illustration. For testing linkage disequilibrium based on pooled genotype data, simulation results show that the imputed Neyman Pearson and Fisher exact tests are less powerful than a Wald-type test based on the observed data maximum likelihood estimator. In conclusion, we caution against the routine use of the fuzzy  p -value approach in latent variable or missing data problems and suggest some viable alternatives.     

A consistent nonparametric test for nonlinear causality—Specification in time series regression

Since the pioneering work by Granger (1969), many authors have proposed tests of causality between economic time series. Most of them are concerned only with “linear causality in mean”, or if a series linearly affects the (conditional) mean of the other series. It is no doubt of primary interest, but dependence between series may be nonlinear, and/or not only through the conditional mean. Indeed conditional heteroskedastic models are widely studied recently. The purpose of this paper is to propose a nonparametric test for possibly nonlinear causality. Taking into account that dependence in higher order moments are becoming an important issue especially in financial time series, we also consider a test for causality up to the Kth conditional moment. Statistically, we can also view this test as a nonparametric omitted variable test in time series regression. A desirable property of the test is that it has nontrivial power against T^1/2-local alternatives, where T is the sample size. Also, we can form a test statistic accordingly if we have some knowledge on the alternative hypothesis. Furthermore, we show that the test statistic includes most of the omitted variable test statistics as special cases asymptotically. The null asymptotic distribution is not normal, but we can easily calculate the critical regions by simulation. Monte Carlo experiments show that the proposed test has good size and power properties.     

Multiple pairwise comparison procedures based on the Lin and Wang test for right censored survival data

When comparing the prognosis of more than two groups in clinical trials, researchers may use multiple comparison procedures to determine which treatments actually differ from one another. Methods of controlling the Family Wise Error (FWE) rate for multiple comparisons of survival curves have received attention in the statistical literature. Adjustments such as Bonferroni, Holm's, Steele's and the closed procedure based on the logrank test have been studied. If hazards cross, the adjustments based on the logrank test may not be the most appropriate. Chi (2005) developed multiple testing procedures based on weighted Kaplan–Meier statistics as these statistics may perform better than the logrank for non‐proportional hazards alternatives. The aim of this research is to propose multiple testing procedures based on the Lin and Wang (2004) statistic for all pairwise comparisons. Simulation studies have shown this statistic can be more powerful than the logrank for certain crossing hazards. Through simulation, the FWE rate and power of the Bonferroni and Holm's adjustments based on the Lin and Wang statistic will be studied. For comparison purposes, the same adjustment procedures based on the logrank and Wilcoxon will be included in the simulations. These methods are applied to data from the Bone marrow transplant registry.     

Monte Carlo Evidence on Cointegration and Causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.     

Generalizing weak instrument robust IV statistics towards multiple parameters,unrestricted covariance matrices and identification statistics

We generalize the weak instrument robust score or Lagrange multiplier and likelihood ratio instrumental variables (IV) statistics towards multiple parameters and a general covariance matrix so they can be used in the generalized method of moments (GMM). The GMM extension of Moreira's [2003. A conditional likelihood ratio test for structural models. Econometrica 71, 1027–1048] conditional likelihood ratio statistic towards GMM preserves its expression except that it becomes conditional on a statistic that tests the rank of a matrix. We analyze the spurious power decline of Kleibergen's [2002. Pivotal statistics for testing structural parameters in instrumental variables regression. Econometrica 70, 1781–1803, 2005. Testing parameters in GMM without assuming that they are identified. Econometrica 73, 1103–1124] score statistic and show that an independent misspecification pre-test overcomes it. We construct identification statistics that reflect if the confidence sets of the parameters are bounded. A power study and the possible shapes of confidence sets illustrate the analysis.