基于LSTAR模型的非线性协整检验

大量的经济理论和实践都表明,宏观经济时间序列经常会出现非平稳和非线性特征,因而在统计分析时,需要进行非线性协整检验。基于逻辑平滑转换自回归(LSTAR)模型将传统的线性协整表述方法拓展为非线性形式,构造实用的检验程序及合适的统计量,利用软件R进行蒙特卡洛模拟给出非线性协整检验统计量的临界值,并通过实际数据分析购买力平价动态系统的非线性协整关系,说明方法的有效性。     

STR-ECM模型非线性协整关系检验问题研究

文章首先运用基于剩余平方和的F统计量实现协整对非协整、线性协整对非线性协整,以及ES-TR-ECM对LSTR-ECM的检验.其次,用蒙特卡洛10000次试验给出F、E、F3统计量的临界值,并模拟数据对有限样本下F1、F2、F3统计量的功效进行检验.最后,用文章的研究与有关研究的统计量进行比较分析,仿真实验表明:提出的检验统计量具有较好的有限样本性质,因此其可行性和适用性较强.     

个体协整条件下面板单位根检验统计量性质研究

 本文通过蒙特卡洛模拟试验,研究了个体间的协整关系对面板单位根检验统计量分布、实际检验水平及检验功效的影响。结果显示：个体间协整关系个数和协整向量均对检验统计量分布产生影响;同时,如果忽视个体间存在的协整关系,继续运用个体间无协整关系条件下检验统计量的临界值进行面板单位根检验,各检验统计量实际检验水平将严重失真。     

非线性财政政策效应下财政赤字 可持续性研究

 财政赤字可持续性检验往往采用线性协整技术来验证跨期预算约束是否成立，但这一检验方法是基于财政政策效应是线性效应理论之上的。在现实中，财政政策既具有凯恩斯效应也具有非凯恩斯效应，财政政策效应是非线性的，财政收支的调整过程也是非线性非对称的。用传统的线性协整技术难以描述财政赤字可持续性过程，本文分析探讨一种用于揭示非平稳时间序列非线性调整过程的模型——两机制门限协整模型，深入研究了该模型的参数估计、检验统计量，并通过自助法(bootstrap)模拟计算其检验统计量临界值及P值。最后利用该模型，揭示了我国财政收支调整是非线性调整过程，并证实了我国财政赤字具有可持续性，但财政赤字规模不应进一步扩大。     

含滞后效应的非线性协整分析

线性协整方法对非平稳经济和金融变量时间序列的计量经济学分析已经比较成熟,但许多宏观经济变量序列以及金融变量序列间的关系往往表现为非线性。文章以NLLS估计为基础提出反映滞后效应的非线性协整回归模型的非线性协整检验方法,统计模拟结果显示该方法具有较小的水平扭曲和较高的势。通过对中国财政支出与城镇居民可支配收入进行实证分析得出两者之间不存在线性协整而存在非线性协整关系,并且这种关系具有滞后效应。     

单位根的秩检验及其应用研究

 本文系统研究了单位根检验的三种非参数秩检验统计量：RDF检验、得分秩( )检验与逆得分秩( )检验,推导了逆得分秩的分布。然后运用Monte Carlo模拟给出了各种秩检验的临界值,将秩检验与传统检验的优劣进行了比较,并将这些方法应用于我国货币需求函数各变量月度序列的实证检验。Monte Carlo模拟和实证应用的结果表明,秩检验与传统单位根检验方法相结合可以发现序列中的非线性特征,这将有助于对时间序列特征的深入分析。     

关于分段非线性型变结构协整的研究

纵现金融时间序列的发展变化研究,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,并提出了分段非线性型变结构的误差校正模型以及变结构点的搜寻的基本方法,最后给出基于Chow统计量的分段非线性型变结构非线性协整的检验方法.     

分数维非线性协整及误差校正模型

本文在分数维和非线性的框架下讨论了经济系统中的长期均衡关系,提出了分数维非线性协整的概念及对应的误差校正模型,基于小波神经网络给出了分数维非线性协整的检验及其误差校正模型的建模方法。实证研究发现中国股市存在分数维非线性协整关系,据此建立了相应的分数维非线性误差校正模型.该模型的预测效果优于带有外生变量的非线性自回归移动平均模型。     

协整平滑转移回归中的线性检验 ——基于完全修正最小二乘法的扩展

 在解释变量内生条件下,Choi,Saikkonen(2004)使用动态最小二乘法估计协整平滑转移回归模型,并基于动态最小二乘的估计结果构造 统计量检验协整向量的非线性。本文系统解析了 的构造并指出其不足,针对这一不足,本文将动态最小二乘法扩展为完全修正的最小二乘法,并进而基于完全修正的最小二乘法估计结果构造 统计量检验协整向量的非线性。本文的仿真试验表明,在有限样本下, 与 的检验势没有显著差异,但 的水平扭曲小于 。     

协整检验的DGP识别

本文用数值模拟的方法比较了不同DGP下协整检验迹统计量的分布特征,分析DGP误设对协整检验结果的重大影响;进而利用我国货币市场和股票市场的数据进行实证分析,通过真实的例子,揭示DGP误设可能导致的各种错误结果。实证分析中还进一步使用递归协整检验和协整关系的约束识别检验,从不同角度显示了协整关系检验结论的稳健性和可靠性。     

No-cointegration test based on fractional differencing: Some Monte Carlo results

This paper examines the use of the t-statistic in the Geweke–Porter-Hudak regression for the estimation of the fractional differencing parameter as a test for cointegration. The critical values of the test statistic are estimated using Monte Carlo methods. The results confirm that the test will over-reject the null hypothesis of no cointegration if the standard-normal critical values are used. The estimated critical values are generally robust to the nuisance parameters in the autoregressive or moving average specification of the error process of the component time series. Exceptions occur when the dependent variable in the cointegration regression follows an autoregressive process with a large positive parameter or a moving average process with a large negative parameter.     

Critical values for testing for a single outlier in a nonlinear regression model

The maximum absolute studentized residual is commonly used for testing for a single outlier in a linear regression model. This test statistic, however, is seldom discussed in a nonlinear regression setting. We simulate the critical values for the tests under various nonlinear models. The associated critical values are found to be very close to one another. Moreover, they are very well approximated using the critical values obtained from F-distributions based on the Bonferroni equations in linear models. The results are promising even in samples of size 6.     

A Multisample Rank Test for Location-Scale Parameters

One of the multisample problems is discussed in this article. A new multisample rank tests based on a k-sample Baumgartner statistic are proposed for testing the location-scale parameters. The exact critical values of proposed statistics are calculated. Simulations are used to investigate the power of proposed statistics for various population distributions.     

Testing for the Null Hypothesis of Cointegration with a Structural Break

In this paper we propose residual-based tests for the null hypothesis of cointegration with a structural break against the alternative of no cointegration. The Lagrange Multiplier (LM) test is proposed and its limiting distribution is obtained for the case in which the timing of a structural break is known. Then the test statistic is extended to deal with a structural break of unknown timing. The test statistic, a plug-in version of the test statistic for known timing, replaces the true break point by the estimated one. We show the limiting properties of the test statistic under the null as well as the alternative. Critical values are calculated for the tests by simulation methods. Finite-sample simulations show that the empirical size of the test is close to the nominal one unless the regression error is very persistent and that the test rejects the null when no cointegrating relationship with a structural break is present. We provide empirical examples based on the present-value model, the term structure model, and the money-output relationship model.     

Testing for the Null Hypothesis of Cointegration with a Structural Break

In this paper we propose residual-based tests for the null hypothesis of cointegration with a structural break against the alternative of no cointegration. The Lagrange Multiplier (LM) test is proposed and its limiting distribution is obtained for the case in which the timing of a structural break is known. Then the test statistic is extended to deal with a structural break of unknown timing. The test statistic, a plug-in version of the test statistic for known timing, replaces the true break point by the estimated one. We show the limiting properties of the test statistic under the null as well as the alternative. Critical values are calculated for the tests by simulation methods. Finite-sample simulations show that the empirical size of the test is close to the nominal one unless the regression error is very persistent and that the test rejects the null when no cointegrating relationship with a structural break is present. We provide empirical examples based on the present-value model, the term structure model, and the money-output relationship model.     

Modified Baumgartner statistics for the two-sample and multisample problems: a numerical comparison

Various non-parametric rank tests based on the Baumgartner statistic have been proposed for testing the location, scale and location–scale parameters. The modified Baumgartner statistics are not suitable for the scale shifts for a two-sample problem. Two modified Baumgartner statistics are proposed by changing the weight function. The suggested statistics are extended to the multisample problem. Some exact critical values of the suggested test statistics are evaluated. Simulations are used to investigate the power of the modified Baumgartner statistics.     

Weighted combinations of rank tests for location-scale alternatives

All existing location-scale rank tests use equal weights for the components. We advocate the use of weighted combinations of statistics. This approach can partly be substantiated by the theory of locally most powerful tests. We specifically investi= gate a Wilcoxon-Mood combination. We give exact critical values for a range of weights. The asymptotic normality of the test statistic is proved under a general hypothesis and Chernoff-Savage conditions. The asymptotic relative efficiency of this test with respect to unweighted combinations shows that a careful choice of weights results in a gain in efficiency.     

Comparison of unweighted and weighted rank based tests for an ordered alternative in randomized complete block designs

In randomized complete block designs, a monotonic relationship among treatment groups may already be established from prior information, e.g., a study with different dose levels of a drug. The test statistic developed by Page and another from Jonckheere and Terpstra are two unweighted rank based tests used to detect ordered alternatives when the assumptions in the traditional two-way analysis of variance are not satisfied. We consider a new weighted rank based test by utilizing a weight for each subject based on the sample variance in computing the new test statistic. The new weighted rank based test is compared with the two commonly used unweighted tests with regard to power under various conditions. The weighted test is generally more powerful than the two unweighted tests when the number of treatment groups is small to moderate.