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。     

非线性协整检验与“费雪效应”机制分析

本文分别在线性Engle-Granger协整模型和非线性指数平滑迁移自回归误差修正模型 (ESTAR-ECM) 的框架下,对我国名义利率与通货膨胀率序列进行了长期均衡关系的检验。发现线性协整模型不能捕捉到我国名义利率与通货膨胀率的长期均衡关系,而对于ESTAR-ECM模型,无论利用商业银行1年期贷款利率还是7天期银行间同业拆借利率作为名义利率的代理变量,均证实名义利率与通货膨胀率具有长期稳定的均衡关系,表明“费雪效应”在我国是成立的。但由于“费雪效应”系数小于1,表明名义利率与通货膨胀率之间仅存在弱的“费雪效应”。其意义在于,我国利率政策对稳定通胀预期、抑制通货膨胀具有一定的正面效应,但由于利率对通货膨胀反应不足,导致完全依靠利率政策控制目前较高的通货膨胀有一定的困难。     

Cointegration Detection Using Dynamic Factor Models

A method of information-criterion-based cointegration detection using dynamic factor models is proposed. The results of the data-based and non data-based Monte Carlo simulations suggest that this method is as effective as conventional hypothesis-testing methods. In the proposed method, an observed multivariate time series is described in terms of common stochastic trends plus stationary autoregressive cycles. Then the best model is selected from among alternative models obtained by changing the number of common stochastic trends, on the basis of information criteria. Consequently, the cointegration rank is determined on the basis of the selected model. Two advantages of the proposed method are also discussed.     

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。     

门限协整套利:理论与实证研究

 不同市场上的同质或相似商品的价格存在长期均衡关系,当价格偏离均衡时,由于套利交易的存在,偏离会迅速回到均衡。在一定的门限值以外,二者服从协整关系,在门限值以内,二者没有协整关系,这种关系称为门限协整。本文在Balke,Fomby(1997)[1]和Hasen（1996）[6]的基础上提出了基于门限向量误差修正模型（T-VECM）的sup-Wald检验,用Bootstrap方法模拟统计量的渐进分布,验证了英国富时指数期货（uk100）和德国法兰克福指数期货（ger30）的门限协整关系,并用Hasen,Seo（2002）[11]提出的极大似然估计方法（MLE）同时估计出门限参数和协整向量,并给出了在这种门限协整关系下进行跨市场无风险套利的策略。     

Inflation and the Divergence of Relative Prices: Evidence from a Cointegration Analysis

The relation between inflation and RPV plays a prominent role in explaining the costs of inflation. This study investigates whether the CPI subcategories drift apart more over a period of high inflation rates than during one of low inflation. The wider dispersion of the subcategories is reflected in an increasing number of common stochastic trends in the system of sub price indices. The results for US data as well as for cross-country comparisons indicate that the influence of inflation on the dispersion of relative prices cannot be revealed by counting cointegrating relations. Thus, the number of stochastic trends or cointegrating relations is not a reliable indicator for the distorting effect of inflation on the dispersion of relative prices.     

A Note on Nonlinear Cointegration,Misspecification, and Bimodality

We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalise to more complicated nonlinear models involving integrated time series.     

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.     

Tests for the Null Hypothesis of Cointegration: A Monte Carlo Comparison

The aim of this paper is to compare the relative performance of several tests for the null hypothesis of cointegration, in terms of size and power in finite samples. This is carried out using Monte Carlo simulations for a range of plausible data-generating processes. We also analyze the impact on size and power of choosing different procedures to estimate the long run variance of the errors. We found that the parametrically adjusted test of McCabe et al. (1997) is the most well-balanced test, displaying good power and relatively few size distortions.     

The Comparison of Performances of Widely Used Cointegration Tests

The aim of this study is to compare performances of commonly cointegration tests used in literature in terms of their empirical power and type I error probabilty for various sample sizes. As a result of the study, it has been found that some tests are not appropriate in testing cointegration in terms of empirical power and type I error probability. As a result of simulation study, λ_max test for any values of ρ and sample sizes have been found most appropriate test in conclusion.     

A Bayesian Approach to Modeling Time-Varying Cointegration and Cointegrating Rank

A multivariate model that allows for both a time-varying cointegrating matrix and time-varying cointegrating rank is presented. The model addresses the issue that, in real data, the validity of a constant cointegrating relationship may be questionable. The model nests the submodels implied by alternative cointegrating matrix ranks and allows for transitions between stationarity and nonstationarity, and cointegrating and noncointegrating relationships in accordance with the observed behavior of the data. A Bayesian test of cointegration is also developed. The model is used to assess the validity of the Fisher effect and is also applied to equity market data.     

Cointegration and direct tests of the rational expectations hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniques, an existing empirical example testing the REH for British manufacturing firms is re-examined and tested over an extended data set.     

Cointegration and direct tests of the rational expectations hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniques, an existing empirical example testing the REH for British manufacturing firms is re-examined and tested over an extended data set.     

英镑与欧元汇率的协整性分析及ECM模型

文章利用欧元汇率及英镑汇率的日数据对两者之间的关系进行了实证研究,在研究的过程中以ECM为模型,其结果显示欧元汇率对英镑汇率短期内变动的影响较大。     

The Demand for Gasoline in China: a Cointegration Analysis

The economic reforms in China since 1979 and consequent increases in disposable income have caused total gasoline consumption to soar nearly 240% between 1980 and 1999. As the growth rate of gasoline consumption is expected to be high due to the increased economic activity resulting from China's re-accession to the WTO, the government must understand the implications for economic growth and balance of payments. Using cointegration techniques, it was found that, between 1980 and 1999, demand for gasoline was relatively inelastic to price changes, both in the short and long terms. The long-run income elasticity was 0.97, implying that the future growth rate of gasoline consumption will be close to the growth rate of the economy, which is predicted to be about 7% per annum from 2001 to 2005, and 5-6% over the decade thereafter.     

基于小波多分辨分析的协整建模理论与方法的扩展;

为讨论经济及金融变量的多尺度行为,描述变量之间在不同时间尺度上的长期均衡关系,将小波多分辨分析引入协整建模理论,提出多分辨协整和多分辨误差校正模型两个概念,给出相应建模方法,克服了传统的协整建模理论无法揭示蕴含在变量内部的多时间尺度信息的缺陷。多分辨协整建模能够更加细致地捕获经济或金融变量在不同时间尺度上的关系,对两大股指的实证研究也支持了这一点。     

基于非线性ECM模型的贝叶斯门限协整研究

 现有门限协整检验方法由于模型似然函数具有多峰、不连续特征,导致冗余参数识别存在困难,最优化计算相对复杂。本文提出基于非线性误差修正模型的贝叶斯门限协整分析,结合参数的后验条件分布设计MCMC抽样方案,进行贝叶斯门限协整检验;并利用Monte Carlo仿真研究了贝叶斯门限协整检验的有限样本性质,发现贝叶斯门限协整检验方法具有良好的有限样本性质。同时,利用不同期限的美国利率序列进行了实证研究,结果发现1个月与3个月利率之间、3个月与6个月利率之间以及3个月与1年利率之间均存在门限协整关系。研究结果表明：贝叶斯门限协整检验方法解决了冗余参数识别的难题,使计算变得相对简单,并提高了估计的精确度和检验的准确性。     

Pitfalls in Estimating Cointegrating Vector when Cointegration Relationship has Nonlinear Adjustment

This article investigates the properties of the estimators of the cointegrating vector when the cointegration error has a nonlinear adjustment. We investigate the properties of three estimators, namely, ordinary least squares (OLS), dynamic OLS (DOLS), and autoregressive distributed lag (ADL) models. Monte Carlo simulation results demonstrate that although all the estimators have consistency under cointegration with a nonlinear adjustment, they suffer from severe size distortions for the t-statistics of the cointegrating vector when the cointegration error has a highly persistent nonlinear adjustment and endogeneity. The results imply that the use of DOLS and ADL for cointegration with nonlinear adjustment cannot sufficiently improve the estimates and size performances.     

信贷约束、风险态度与家庭资产选择

本文运用中国家庭金融调查数据（CHFS）,从信贷约束与风险态度两个方面研究其对家庭资产的参与及配置影响。研究发现,在控制其他因素情况下,家庭信贷约束会增加家庭风险厌恶程度;受到信贷约束的家庭,其房产持有概率和房产市值均显著下降;其股票持有概率会显著下降,但对其持有股票市值影响并不显著;受到信贷约束的家庭,其购买商业保险的概率偏低;家庭风险态度对家庭房产选择的影响不显著;对股票资产的持有概率和持有量均产生负向影响,对商业保险资产的持有则产生显著正向影响。