基于回归模型的列联表独立性检验

本文利用回归模型设计列联表的独立性检验方法。利用虚拟变量技术将列联表定性因素转化为因变量和自变量,构造Wald统计量对二维列联表独立性进行检验,并在此基础上进一步设计方法对高维列联表的独立性问题进行检验。该方法能够将列联表的一些常见的检验问题纳入到一个统一的框架下来进行,有助于人们更好地理解和使用。     

数据抽样频率对Granger因果关系的影响

研究目标：分析数据抽样频率对Granger因果关系的影响。研究方法：依据基础变量之间是否存在Granger因果关系,本文分三种情形研究系统抽样和时期归并对变量之间Granger因果关系的影响。进而,依据季度数据,利用靳庭良（2013）提出的ECM-DM检验程序,对我国改革开放以来货币供给量与价格水平、产出之间的Granger因果性重新进行了检验。研究发现：数据抽样频率是否影响变量之间的Granger因果性与基础变量之间的Granger因果性、模型的滞后阶数及其预测误差之间的同期相关性有关。研究创新：本文给出了Granger因果关系是否随着数据抽样频率变化的一般判定条件。研究价值：为解释依据不同抽样频率数据所得Granger因果关系检验结果存在的差异,提供了基本理论支撑。     

Granger因果关系检验应用中的若干问题

分析了实践中应用Granger因果关系检验存在的一些问题,如信息遗漏,变量变换改变因果关系的性质、变量单整性对检验程序的影响以及检验模型的选择等,并提出在线性投影上有初步证据的因果概念。进而,应用单整变量之间Granger因果关系的一般检验程序对1978~2013年我国货币供给量与价格水平、产出之间的Granger因果性重新进行检验。     

基于MF-VAR的混频数据非线性格兰杰因果关系检验

研究目标：提出一种针对混频数据非线性格兰杰因果关系检验的方法。研究方法：在混频向量自回归模型（MFVAR）的基础上提出构造Wald统计量,通过模拟实验和实证研究考察统计量的性质。研究发现：模拟实验结果表明该检验相对于其他混频检验和低频检验具有更优的性质,并且对检验式误设具有稳健性。进一步针对中国经济增长与消费者信心因果关系的实证研究证明,混频检验与低频检验得出的结论有很大差异,混频检验得出的结论更符合经济理论。研究创新：利用自助法修正统计量在有限样本下产生的水平扭曲,利用典型相关分析实现了数据降维。研究价值：在检验不同频率变量之间的非线性格兰杰因果关系时避免了信息损失和虚增。     

倾向指数匹配方法：变量选择和模型设定问题

本文讨论了倾向指数匹配方法估计中变量选择和模型设定对估计偏差的影响。发现条件独立性假设是正确估计平均因果效应的关键。如果潜在结果影响选择,倾向指数匹配方法是无法消除估计偏差的。在满足条件独立性假设的前提下,倾向指数模型的变量选择非常重要。如果加入与倾向指数无关的变量,不会造成估计偏差,反而有时会提高估计精度,如果遗漏决定选择的重要变量,将会造成估计偏差。最后,结合案例给出一种条件独立性的检验方法。     

单整变量之间Granger因果关系的一种检验程序

本文系统研究了含有单整变量的变量之间Granger因果关系基于OLS估计的检验方法,将适用于存在（1,1）阶协整关系的I(1)变量之间Granger因果关系检验的Engle和Granger（1987）两步程序,扩展到了存在协整关系的高阶单整变量的情形,并提出了含有单整变量的变量之间Granger因果关系检验的一般程序。     

中国宏观经济变量的时间趋势属性研究

本文提出了一种新的时间趋势属性的检验方法,该方法融合了非线性模型与线性模型。本文构建了三个Wald类检验统计量及一个稳健检验统计量,推导出了这些统计量的极限分布并分析了其有限样本下的统计性质。应用该检验程序,本文分析了我国24个重要宏观经济变量的时间趋势属性,结果表明,其中22个经济变量具有非线性平滑转移特征,其时间趋势属性表现为确定性。     

局部平稳性未知条件下STAR模型的线性性检验

本文讨论了局部随机游走STAR模型、局部随机趋势STAR模型的线性性检验问题,构造了Wald类检验统计量,推导出了这些统计量的极限分布,并分析了这些统计量有限样本下的统计特性;本文提出了在局部平稳性未知的条件下,进行STAR模型的线性性检验方法,构建了稳健的检验统计量。检验功效与检验水平分析表明,该统计量具有良好的检验水平及较高的检验功效。     

我国宏观经济运行动态模拟与预测

一、研究的意义。监测我国宏观经济运行状况是否良好。经济是否能持续、快速、稳定、健康地发展。一直是我国学和政府时刻关注的焦点。而这一评价过程又是通过运用诸多宏观经济指标及指标间的联系来衡量的。在以往大多数研究中，是通过逐个建立单个指标对其他一个或多个指标相应的单一方程模型，对未来有关宏观经济指标进行预测。但是单方程模型有一个隐含的假定，就是被解释变量与解释变量之间如果有因果关系。则这种关系是单向的，即解释变量是原因，被解释变量是结果。单方程模型只是从一个方向解释了变量间的因果关系，因变量与解释变量之间没有反馈式的关系。但是在国民经济运行中，根据这种单向因果关系建立模型进行预测几乎是没有意义的。因为解释变量与被解释变量之间有着双向或联立关系，这时就需要采用联立方程模型．它可以使我们考虑多个变量之间的相互关系．通常这类模型由一组回归方程构成。这样，在估计一个方程的参数时就可以运用方程组中其他方程所提供的信息。我们可以运用一些统计软件来对这些方程进行估计。本采用计量经济学软件Eviews 3．1来对联立方程进行估计。在单方程模型里。普通最小二乘法是最适合这种模型的．但是两个或多个内生变量的同时存在需要我们使用另外的建模和估计方法。因为方程的联立性会使普通最小二乘法得到的参数估计量不一致。因为在联立方程模型中，一个方程中的内生变量往往又影响另一个方程中的其他变量，因此误差项与内生变量相关，所以普通最小二乘估计量也将是有偏和不一致的。     

我国宏观经济时间序列的结构突变与宏观经济运行态势分析

院文章基于双变量结构突变模型,利用Andrews 检验统计量和Bai 子样本过程以及Hansen 异方差固定回归元自举法对我国主要宏观经济变量之间关系的稳定性进行了检验.研究发现,自20 世纪90 年代以来,在金融危机、体制改革等外部冲击和内部冲击的双重作用和影响下,我国主要宏观经济指标,例如消费、投资和政府支出等与国内生产总值之间的关系均发生了不同程度的结构突变,这意味着我国经济周期波动态势也出现了转变.     

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.     

A new statistic and practical guidelines for nonparametric Granger causality testing

In this paper we introduce a new nonparametric test for Granger non-causality which avoids the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664]. After illustrating the problem by showing that rejection probabilities under the null hypothesis may tend to one as the sample size increases, we study the reason behind this phenomenon analytically. It turns out that the Hiemstra–Jones test for the null of Granger non-causality, which can be rephrased in terms of conditional independence of two vectors X and Z given a third vector Y, is sensitive to variations in the conditional distributions of X and Z that may be present under the null. To overcome this problem we replace the global test statistic by an average of local conditional dependence measures. By letting the bandwidth tend to zero at appropriate rates, the variations in the conditional distributions are accounted for automatically. Based on asymptotic theory we formulate practical guidelines for choosing the bandwidth depending on the sample size. We conclude with an application to historical returns and trading volumes of the Standard and Poor's index which indicates that the evidence for volume Granger-causing returns is weaker than suggested by the Hiemstra–Jones test.     

Testing for structural change in regression quantiles

Most studies in the structural change literature focus solely on the conditional mean, while under various circumstances, structural change in the conditional distribution or in conditional quantiles is of key importance. This paper proposes several tests for structural change in regression quantiles. Two types of statistics are considered, namely, a fluctuation type statistic based on the subgradient and a Wald type statistic, based on comparing parameter estimates obtained from different subsamples. The former requires estimating the model under the null hypothesis, and the latter involves estimation under the alternative hypothesis. The tests proposed can be used to test for structural change occurring in a pre-specified quantile, or across quantiles, which can be viewed as testing for change in the conditional distribution with a linear specification of the conditional quantile function. Both single and multiple structural changes are considered. We derive the limiting distributions under the null hypothesis, and show they are nuisance parameter free and can be easily simulated. A simulation study is conducted to assess the size and power in finite samples.     

Granger-causality in multiple time series

The concept of Granger-causality is formulated for a finite-dimensional multiple time series. Special attention is given to causality patterns in autoregressive series, and it is shown how these patterns can be tested under quite general assumptions using a χ² statistic. The power of the test is discussed, and it is shown that the χ² statistic results from a Lagrange multiplier test in the Gaussian case. The causality test is tried both on artificial data and some economic time series. Finally we consider the problem of constrained estimation in models with a known causality structure.     

International market links and volatility transmission

This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality of the test statistic as well as the first-order validity of the bootstrap analog. Finally, we uncover significant volatility spillovers between the stock markets in China, Japan, UK and US.     

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.     

A new instrumental method for dealing with endogenous selection

This paper develops a new method for dealing with endogenous selection. The usual instrumental strategy based on the independence between the outcome and the instrument is likely to fail when selection is directly driven by the dependent variable. Instead, we suggest to rely on the independence between the instrument and the selection variable, conditional on the outcome. This approach may be particularly suitable for nonignorable nonresponse, binary models with missing covariates or Roy models with an unobserved sector. The nonparametric identification of the joint distribution of the variables is obtained under a completeness assumption, which has been used recently in several nonparametric instrumental problems. Even if the conditional independence between the instrument and the selection variable fails to hold, the approach provides sharp bounds on parameters of interest under weaker monotonicity conditions. Apart from identification, nonparametric and parametric estimations are also considered. Finally, the method is applied to estimate the effect of grade retention in French primary schools.     

A note on a specification test of independence

In a recent paper Zheng (1997a) proposed a new specification test of independence between two random vectors by the kernel method. He showed asymptotic normality under the hypothesis and local alternatives. The present work investigates the asymptotic distribution of the corresponding test statistic under fixed alternatives. In this case asymptotic normality of a standardized statistic is still valid but with a different rate of convergence. Received: January 1999     

The power of bootstrap and asymptotic tests

The bootstrap discrepancy measures the difference in rejection probabilities between a bootstrap test and one based on the true distribution. The order of magnitude of the bootstrap discrepancy is the same under the null hypothesis and under non-null processes described by Pitman drift. If the test statistic is not an exact pivot, critical values depend on which data-generating process (DGP) is used to determine the null distribution. We propose using the DGP which minimizes the bootstrap discrepancy. We also show that, under an asymptotic independence condition, the power of both bootstrap and asymptotic tests can be estimated cheaply by simulation.