吉林省城镇居民消费与收入关系的协整研究

一、协整理论与消费函数协整(cointegration)这一概念,最早是在20世纪80 年代,由2003年的诺贝尔经济学奖获得者恩格尔 (Engle)和格兰杰(Granger)提出,后经众多的计量经济学家发展成为协整理论和误差修正模型(ECM)。协整理论认为,两个或多个非平稳经济时问序列的某种线性组合可能是平稳的,若从经济意义上来理解,则说明这些经济变量问存在着长期均衡关系。而认识经济变量间的这种长期均衡关系,对于掌握经济规律、制定经济     

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。     

上证指数与深成指间协整关系的非参数检验

一、引言 在时间序列中常会发现两个变量存在一种长期稳定关系,C.J.Granger把这种长期稳定关系称为"协整关系".传统的协整分析方法是通过对序列差分将其转化为平稳序列,得出其中的线性均衡关系,这种协整关系可称为线性协整.线性协整的建模理论是从实际的数据生成过程出发,在非平稳序列中寻找可能存在的长期线性均衡关系,以建立序列的结构模型,从而反映序列的运行机制.     

基于VAR模型的旅游乘数效应研究

本文根据向量自回归理论与Johansen协整理论,通过建立经济增长与入境旅游花费之间的多变量向量自回归(VAR)模型,研究中国入境旅游花费与经济增长之间的协整关系,从而对旅游乘数效应作出实证检验.     

区域经济增长与能源消费关系的实证检验——基于中国八大经济区域的面板数据

文章将面板数据模型应用到我国八人经济区域经济增长与能源消费关系的研究中,利用面板单位根检验、面板协整检验对两经济变量的长期均衡关系进行经验检验,并在面板数据模型的类型、识别和估计问题拘分析基础上建立了八人经济区域经济增长与能源消费的变系数模型,得出不同地区能源消费特特对经济增长速度的’长期影响程度不同,从实汇角度说朋了在应用面板单位根检验与面板协整检验过程中应注意的几点问题。     

基于国债回购市场数据的利率预期理论检验

本文通过选用上交所国债回购市场(http://www.sse.com.cn)1996年7月22日到2005年12月13日的2164个日度数据,包括R003、R007、R014、R028、R091和R182六种不同到期期限的利率,利用单位根和多变量协整方法来检验利率预期理论在我国国债回购市场上的有效性,并采用误差修正模型对不同到期期限利率之间的内在驱动关系进行了建模估计和相应的解释。     

对外贸易与经济增长关系的实证检验

为了探究当前形势下我国对外贸易与经济增长的关系,文章选取1978～2012年出口额、进口额和 国内生产总值GDP的实际数据为样本,运用协整分析以及VAR模型对两个变量的关系进行了研究.实证结果表明,在短期,中国的出口贸易、进口贸易是经济增长的格兰杰原因;在长期,出口、进口与经济增长之间存在长期稳定的协整关系.     

湖北省农村居民消费与收入关系的实证分析

一、湖北省农村居民消费与收入的协整分析 协整是指变量之间存在的长期均衡关系,可利用协整理论来检验农村居民的消费与收入之间是否存在这种长期稳定的均衡关系.     

我国经济增长与金融深度的关系检验

文章运用协整检验和格兰杰因果检验方法,研究了我国经济增长与金融深度的关系。协整检验结果表明我国的经济增长与金融深度之间不存在长期稳定的均衡关系;格兰杰因果检验结果表明我国的经济增长与金融深度之间存在着单向的由经济增长指向金融深度的因果关系。并且,首次运用阈值效应对此结果给出解释。     

中国进出口与经济增长关系的实证研究

本文根据1978-2001年中国的经济数据,利用协整和误差校正模型对中国对外贸易与经济增长关系进行Granger因果关系检验,分析中国进口、出口增长与经济增长之间是否存在协整关系;在存在协整关系的情况下,从短期和长期来看各变量之间是否构成因果关系以及因果关系的性质.     

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。     

Econometric tests of rationality and market efficiency

Many economic theories give rise to restrictions between the future rational expectations of a set of variables. This paper describes how such theories can be tested from vector time series models. Particular attention is given to problems of nonstationarity and the use of the concept of cointegration in the modeling and testing procedure.     

Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures

In this paper, we propose a Bayesian partition modeling for lifetime data in the presence of a cure fraction by considering a local structure generated by a tessellation which depends on covariates. In this modeling we include information of nominal qualitative variables with more than two categories or ordinal qualitative variables. The proposed modeling is based on a promotion time cure model structure but assuming that the number of competing causes follows a geometric distribution. It is an alternative modeling strategy to the conventional survival regression modeling generally used for modeling lifetime data in the presence of a cure fraction, which models the cure fraction through a (generalized) linear model of the covariates. An advantage of our approach is its ability to capture the effects of covariates in a local structure. The flexibility of having a local structure is crucial to capture local effects and features of the data. The modeling is illustrated on two real melanoma data sets.     

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。     

Comment on testing for spurious and cointegrated regressions: a wavelet approach

In a recent paper, Leong and Huang [6] proposed a wavelet-correlation-based approach to test for cointegration between two time series. However, correlation and cointegration are two different concepts even when wavelet analysis is used. It is known that statistics based on non-stationary integrated variables have non-standard asymptotic distributions. However, wavelet analysis offsets the integrating order of non-stationary series so that traditional asymptotics on stationary variables suffices to ascertain the statistical properties of wavelet-based statistics. Based on this, this note shows that wavelet correlations cannot be used as a test of cointegration.     

An illustration of the causality relation between government spending and revenue using wavelet analysis on Finnish data

Quarterly data for the period 1960:1 to 1997:2, conventional tests, a bootstrap simulation approach and a multivariate Rao's F-test have been used to investigate if the causality between government spending and revenue in Finland was changed at the beginning of 1990 due to future plans to create the European Monetary Union (EMU). The results indicate that during the period before 1990, the government revenue Granger-caused spending, while the opposite happened after 1990, which agrees better with Barro's tax smoothing hypothesis. However, when using monthly data instead of quarterly data for almost the same sample period, totally different results have been noted. The general conclusion is that the relationship between spending and revenue in Finland is still not completely understood. The ambiguity of these results may well be due to the fact that there are several time scales involved in the relationship, and that the conventional analyses may be inadequate to separate out the time scale structured relationships between these variables. Therefore, to investigate empirically the relation between these variables we attempt to use the wavelets analysis that enables us to separate out different time scales of variation in the data. We find that time scale decomposition is important for analysing these economic variables.     

中国货币政策中介目标选择问题的实证研究

货币政策中介目标的选择问题,是货币政策研究中的重要问题之一,同时也是制定和实施货币政策的关键性步骤。对该目标选择是否正确,不仅关系到货币政策最终目标能否实现,而且对货币政策有效性程度的判断,都具有十分重要的意义。改革开放以来,该问题在我国已得到了广泛...     

Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMMs). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study to jointly model questionnaire-based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.     

基于半参数混频误差修正模型的中国CPI预测研究

考虑宏观经济变量具有明显的非线性特征,将非线性误差修正项引入存在协整关系的非平稳混频数据抽样（MIDAS）模型中,构建半参数混频数据抽样误差修正（SEMI-ECM-MIDAS）模型。使用广义似然比（GLR）检验,拓展了混频数据下模型函数形式的一致性检验问题。模拟结果表明SEMI-ECM-MIDAS模型对存在非线性误差修正机制的数据具有显著的预测优势。最后使用该模型研究中国股票市场周度数据、广义货币发行量月度数据和国际原油市场月度数据对中国CPI的短期预测效果。基于AIC准则,对包含半参数模型在内的4种混频数据抽样模型和2种同频模型的连续预测效果进行了全面的比较。研究结果发现：GLR检验表明误差修正项具有明显的非线性特征且在回归中具有显著的反向修正机制,无论采用递归样本、滚动样本还是固定样本,本文提出的SEMI-ECM-MIDAS模型在进行连续预测时均具有最优的预测精度,且预测结果不受混频动态协整关系选择的影响。     

Lagrance-multiplier tersts for weak exogeneity: a synthesis

This paper unifies two seemingly separate approaches to test weak exogeneity in dynamic regression models with Lagrange-mulptiplier statistics. The first class of tests focuses on the orthogonality between innovations and conditioning variables, and thus is related to the Durbin-Wu-Hausman specification test. The second approach has been developed more recently in the context of context of cointegration and error correction models, ad concentrates on the question whether the conditioning variables display error correction behaviour. It is shown that the vital difference between the two approaches stems from the choice of the parmeters of interest. A new test is derived, which encompasses both its predecessors. The test is applied to an error correction model of the demand for money in Switzerland.