Variance (Non) Causality in Multivariate GARCH

This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH. By the introduction of a multiplicative causality impact function, the variance causality effects becomes directly interpretable and can therefore be used to detect both the existence of causality and its direction; notably, the proposed model allows for increasing and decreasing variance effects. An empirical application evidences negative causality effects between returns and volume of an Italian stock market index future contract.     

中国产业结构变动与就业水平的实证研究

文章运用时间序列和计量模型研究了中国产业结构与就业水平之间的发展关系.首先运用格兰杰因果检验分析了第一、二、三次产业的产值与其就业之间的因果影响关系,继而从产值与就业两个角度定量分析了第一、二、三次产业各自对总就业水平的贡献,得出结论:第一产业和第三产业是劳动力就业的决定因素,而第二产业不是就业的决定因素;从产值角度来看,第三产业对劳动力就业的贡献最大,从就业角度来看,第一产业的就业对总体就业水平的贡献最高,其次是第三产业,而第二产业的就业对总体就业水平的贡献最小.最后根据研究结论提出相应的建议措施.     

我国房地产市场财富效应的实证分析

本文应用格兰杰因果关系检验和协整分析方法,对我国房地产市场的财富效应作了实证分析。实证结果表明,我国房地产市场不具有财富效应而仅具有替代效应。     

An analytic method for randomized trials with informative censoring: Part 1

Consider a randomized trial in which time to the occurrence of a particular disease, say pneumocystis pneumonia in an AIDS trial or breast cancer in a mammographic screening trial, is the failure time of primary interest. Suppose that time to disease is subject to informative censoring by the minimum of time to death, loss to and end of follow-up. In such a trial, the censoring time is observed for all study subjects, including failures. In the presence of informative censoring, it is not possible to consistently estimate the effect of treatment on time to disease without imposing additional non-identifiable assumptions. The goals of this paper are to specify two non-identifiable assumptions that allow one to test for and estimate an effect of treatment on time to disease in the presence of informative censoring. In a companion paper (Robins, 1995), we provide consistent and reasonably efficient semiparametric estimators for the treatment effect under these assumptions. In this paper we largely restrict attention to testing. We propose tests that, like standard weighted-log-rank tests, are asymptotically distribution-free -level tests under the null hypothesis of no causal effect of treatment on time to disease whenever the censoring and failure distributions are conditionally independent given treatment arm. However, our tests remain asymptotically distribution-free -level tests in the presence of informative censoring provided either of our assumptions are true. In contrast, a weighted log-rank test will be an -level test in the presence of informative censoring only if (1) one of our two non-identifiable assumptions hold, and (2) the distribution of time to censoring is the same in the two treatment arms. We also extend our methods to studies of the effect of a treatment on the evolution over time of the mean of a repeated measures outcome, such as CD-4 count.     

Testing Wagner's Law in India: A cointegration and causality analysis

The present study empirically analyzes the validity of Wagner's Law for Indian economy. With the use of annual time series data from 1970–71 to 2013–14, all the six versions of Wagner's Law have been analyzed to test the relationship between government expenditure and gross domestic product. Wagner's Law states that the economic growth is the causative factor of the growth of the public expenditure. The study applied the unit root test and cointegration test to find the long-run relationship between government expenditure and gross domestic product. The present study employed the various econometric techniques such as unit root test, cointegration, and causality analysis for empirical analysis. The empirical analysis under study inferred mixed results of Wagner's Law for Indian economy, where four versions, namely Peacock, Gupta, Guffman, and Musgrave, found valid for Indian economy. The study concluded that the Wagner's Law is valid for the Indian economy except the Pryor and Mann Versions of the Wagner's Law.     

Causal structure among US corn futures and regional cash prices in the time and frequency domain

This study investigates causal structure among daily Chicago Board of Trade corn futures prices and seven regional cash series from Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas for January 2006–March 2011. Their wavelet transformed series are further analyzed for causal relationships at different time scales. Empirical results indicate no causality among states or between the futures and a cash series for time scales shorter than one month. As scales increase but do not exceed a year, bidirectional causal flows are determined among all prices. The information leadership role of the futures against a cash price is identified for the scale longer than one year and raw series, at which no interstate causality is found.     

Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study

Previous literature has shown that the addition of an untested surplus-lag Granger causality test can provide highly robust to stationary, non stationary, long memory, and structural break processes in the forcing variables. This study extends this approach to the partial unit root framework by simulation. Results show good size and power. Therefore, the surplus-lag approach is also robust to partial unit root processes.     

Estimating school effectiveness with student growth percentile and gain score models

Measuring school effectiveness using student test scores is controversial and some methods used for this can be inaccurate in some situations. The validity of two statistical models – the Student Growth Percentile (SGP) model and a multilevel gain score model – are evaluated. The SGP model conditions on previous test scores thereby unblocking a backdoor path between true school/teacher effectiveness and student test scores. When the product of the coefficients that make up this unblocked backdoor path is positive, the SGP estimates can be inaccurate. The accuracy of the multilevel gain score model was not associated with the product of this backdoor path. The gain score model appears promising in these situations where the SGP and other covariate adjusted models perform poorly.     

The Effects of Detrending in Granger Causality Tests

Nonstationary time series are frequently detrended in empirical investigations by regressing the series on time or a function of time. The effects of the detrending on the tests for causal relationships in the sense of Granger are investigated using quarterly U.S. data. The causal relationships between nominal or real GNP and M1, inferred from the Granger–Sims tests, are shown to depend very much on, among other factors, whether or not series are detrended. Detrending tends to remove or weaken causal relationships, and conversely, failure to detrend tends to introduce or enhance causal relationships. The study suggests that we need a more robust test or a better definition of causality.     

Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

In this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.