一种新的阈值协整检验方法的仿真研究

文章基于ECM提出一个新的阈值协整检验方法,目的在于把部分协整从阈值协整中区分开来,并对检验法的检验水平和检验势进行仿真研究。仿真研究发现:首先该统计量采用固定回归元自助法具有较高的检验势,但具有较严重的检验水平扭曲,因此会增大弃真的概率。其次采取仿真临界值的检验法会随着数据序列持久性的增强检验势呈下降趋势,但EG两步法下降得更快。     

中国费雪效应的门限协整检验

由于中国费雪效应的研究结果具有很大的不一致性,结合中国1991年1月至2008年12月之间的数据,应用可以刻画变量间非线性均衡关系的门限协整理论检验费雪效应,研究结果显示：第一,中国的名义利率与通货膨胀率均为单位根过程,二者之间不存在线性协整关系,而是存在两个门限值的门限协整关系;第二,当通货膨胀率小于-0．8％时,中国费雪效应不存在,而当通货膨胀率在-0．8％～12．03％2;间时,中国存在值为0．42的部分费雪效应;当通货膨胀率大于12．03％时,中国存在值为0．05的部分费雪效应。     

辽宁省对外贸易与经济增长的协整及因果关系检验

本文采用1978-2002年辽宁省的进口、出口和GDP的数据,利用协整检验和Granger因果关系检验等计量方法,对进口和出口与GDP的关系进行了检验,结果发现它们之间存在长期稳定的关系,但出口与GDP,进口与GDP之间仅存在单向因果关系,即经济增长是进、出口增长的原因,而进、出口的增长并不是经济增长的原因。     

基于聚类分析与协整检验的A股市场统计套利策略

文章通过聚类分析和协整分析,建立量化的股票筛选模型,用于发现和验证两只高度相关的股票价格序列在短期内微小但是持续有规律的动态变化,并通过建立统计套利交易系统利用其进行套利。通过我国A股市场上对同行业内任意选择的股票组合的检验发现:相对于任意选择的股票,通过系统聚类的一类股票价格序列,相关程度更高,且在通过聚类分析得到的股票组合中,具有协整关系的股票组合的累计收益高于不具有协整关系的股票组合。     

我国居民消费与GDP的协整检验及误差修正模型

本文运用协整分析理论,对我国居民消费总额与GDP之间的关系进行了探讨,得出二者存在长期均衡的关系,并建立了误差修正模型。经过ECM误差修正之后得出结论。从长期来看,GDP对居民消费的影响是根本性的。然而从短期分析。当期GDP对下期消费的调节作用是较大的。     

基于准残差的Panel协整检验

 当检验方程含有确定性趋势时,基于残差的Panel协整检验通过全样本回归来消除确定性趋势的方法会导致检验统计量出现Nickell偏差,必须使用标准化系数进行修正,但这些标准化系数却依赖于真实设定参数。本文将Breitung和Das（2005）等使用部分样本回归消除确定性趋势的方法应用到Panel协整检验,提出了基于准残差的Panel协整检验,我们证明了这种新的检验统计量的渐近正态性,模拟的结果也表明其具有非常良好的小样本表现。     

我国财政支出与收入关系的协整研究

文章利用1950～1999年的统计资料,对我国财政支出与收入之间的关系进行协整研究。结果表明:从长期看,建国50年来,我国财政支出与收入之间存在稳定的均衡关系;从短期看,收入的短期变动对支出有较强的影响,二者之间的缺口能够在下一期得到显著的调整。     

协整技术在股票开盘价预测中的应用

本文从集合竞价的交易机制的信息传递角度出发,利用因果检验方法找出影响开盘价形成的因素,认为前一日交易的收盘价是最主要的影响因素.然后利用协整技术建立关于开盘价和收盘价的协整模型,并运用该模型进行事后预测,并对其有效性进行了检验.最后,对该模型成立的原因进行经济分析,并指出其股票价格的发现功能.     

人民币汇率购买力平价多变量Johansen协整检验

为了研究购买力平价在我国的适用性问题,文章运用Johansen极大似然估计法对名义汇率与中美物价指数之间的多变量协整关系进行检验,研究结果表明:购买力平价理论在我国还不能很好地解释人民币汇率的变动规律.不能单从购买力平价理论来判断人民币汇率水平.     

我国制造业对外贸易与经济增长的协整及因果关系检验

文章利用1992～2006年我国制造业进出口贸易的统计数据,采用协整检验和Granger因果检验对过去15年间我国制造业对外贸易的发展与经济增长的关系进行了实证分析.研究表明,我国制造业贸易总额与GDP之间、进口与GDP之间以及出口与GDP之间均存在长期的唯一协整关系及单向的Granger因果关系.     

协整参数的自举推断

 针对完全修正最小二乘(full-modified ordinary least square,简称FMOLS）估计方法,给出一种协整参数的自举推断程序,证明零假设下自举统计量与检验统计量具有相同的渐近分布。关于检验功效的研究表明,虽然有约束自举的实际检验水平表现良好,但如果零假设不成立,自举统计量的分布是不确定的,因而其经验分布不能作为检验统计量精确分布的有效估计。实际应用中建议使用无约束自举,因为无论观测数据是否满足零假设,其自举统计量与零假设下检验统计量都具有相同的渐近分布。最后,利用蒙特卡洛模拟对自举推断和渐近推断的有限样本表现进行比较研究。     

The Estimation and Inference of a Panel Cointegration Model with a Time Trend

This article investigates the asymptotic properties of coefficient estimators in the panel cointegration model with a time trend. We find that the bias of OLS estimator for the slope coefficient in the panel cointegration model with a time trend is distinct from that in the panel cointegration model without a time trend. Meanwhile, the variance of the limiting distribution for the slope coefficient is larger in the panel cointegration model with a time trend than without a time trend.     

Identification and Inference With Many Invalid Instruments

We study estimation and inference in settings where the interest is in the effect of a potentially endogenous regressor on some outcome. To address the endogeneity, we exploit the presence of additional variables. Like conventional instrumental variables, these variables are correlated with the endogenous regressor. However, unlike conventional instrumental variables, they also have direct effects on the outcome, and thus are “invalid” instruments. Our novel identifying assumption is that the direct effects of these invalid instruments are uncorrelated with the effects of the instruments on the endogenous regressor. We show that in this case the limited-information-maximum-likelihood (liml) estimator is no longer consistent, but that a modification of the bias-corrected two-stage-least-square (tsls) estimator is consistent. We also show that conventional tests for over-identifying restrictions, adapted to the many instruments setting, can be used to test for the presence of these direct effects. We recommend that empirical researchers carry out such tests and compare estimates based on liml and the modified version of bias-corrected tsls. We illustrate in the context of two applications that such practice can be illuminating, and that our novel identifying assumption has substantive empirical content.     

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。     

Cointegration and Threshold Adjustment

This article proposes an extension to the Engle–Granger testing strategy by permitting asymmetry in the adjustment toward equilibrium in two different ways. We demonstrate that our test has good power and size properties over the Engle–Granger test when there are asymmetric departures from equilibrium. We consider an application—namely, whether there exists cointegration among interest rates for instruments with different maturities. This issue has been widely tested with mixed results. We argue that either cautious policy, or possibly opportunistic behavior on the part of the Federal Reserve implies that an equilibrium relationship between short- and long-term interest rates exists but that adjustments from disequilibrium are asymmetric in nature. Empirical tests using U.S. yields confirm the asymmetric nature of error correction among interest rates of different maturities.     

Cointegration and Long-Horizon Forecasting

We consider the forecasting of cointegrated variables, and we show that at long horizons nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box–Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.     

Johansen和Juselius协整检验应注意的几个问题

Johansen和Juselius的似然比检验多变量协整关系的方法在实证分析中得到了广泛应用。在总结该方法的基础上,针对国内使用该方法存在比较混乱的状况指出了一些注意事项,譬如根据经济时间序列的数据生成过程选择确定性成分,检验临界值的使用以及协整关系个数的非唯一性等问题,还简要论述了阶数的确定、外生性与因果关系检验等问题,最后指出了该检验的一些不足。通过对上述问题的讨论,试图为实证研究人员在使用该方法时提供简单有效的指导性建议。     

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926 Yule , G. U. ( 1926 ). Why we do sometimes get nonsense-correlations between time-series? A study in sampling and the nature of time-series (with discussion) . J. Roy. Statist. Soc. 89 : 1 – 64 .[Crossref] , [Google Scholar]). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999 Bauwens , L. , Lubrano , M. , Richard , J.-F. ( 1999 ). Bayesian Inference in Dynamic Econometric Models . Oxford : Oxford University Press . [Google Scholar]) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful and convincing results”.

The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.     

Relative Commodity Prices and Cointegration

The Prebisch–Singer hypothesis of a secular decline in the relative price of primary commodities is reassessed, on the basis of recently available time series data, for 1900–1986. Special attention is given to adequate dynamic modeling of price fluctuations. Primary and manufactured-goods prices are found to be cointegrated. The hypothesis of stationarity for the long-run relative-price movements cannot be rejected. The empirical evidence does not support the Prebisch–Singer hypothesis.