Power and causality

Quality & Quantity - The article first establishes the conceptual link between power and causality. It then attempts to review the literature on power from the causal modelling perspective. The...     

Testing causality: Some caveats and a suggestion

The Granger concept of causality is defined within the framework of dynamic economic systems in terms of the predictability criterion. The concept has however been often wrongly applied to static systems. The test procedures for Granger causality due to Granger, Sims, Haugh and Pierce provide grossly inconclusive and often conflicting if not misleading results. These problems are illustrated with the data on profits and investment for Canada and the U.S.A. Further, it is suggested that the predictability criterion, if interpreted in terms of conventional forecasting methods, would enable us to provide more conclusive results. The problems assume added importance as the recent vintage of econometric modelling techniques heavily rely on these causality tests.     

Short and long run causality measures: Theory and inference

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424–459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h$h$ as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099–1125; Dufour, J.-M., Pelletier, D., Renault, É., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337–362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h$h$ of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304–313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.     

Asymmetric causality in-mean and in-variance among equity markets indexes

The aim of this study is to research asymmetric causality in-mean and variance among financial markets. The methodology used has several advantages: the estimation is jointly and not by pairs of variables, it identifies whether the causality is asymmetric (different effects from positive and negative returns), and, in the case of bidirectional relationships, it allows us to test whether the effect is the same in both directions. This study provides evidence of causality in-mean and variance among the daily returns of stock markets indexes from January 1, 1997 to December 31, 2014. The indexes are grouped by geographical areas to avoid problems of asynchrony with partially overlapping markets. The main results show that the causalities in-mean and in-variance are asymmetric with a different effect from positive and negative returns. Notably, by geographical areas, the Indian index SENSEX (symmetric) and the Chinese Shanghai Stock Exchange (asymmetric) are causatives in-mean for Asia, The Italian MIB 30 (asymmetric) is causative in-variance for Europe, and the USA DOW JONES 100 (symmetric) and STANDARD&POORS 500 (asymmetric) indexes are causatives in-mean and in-variance for America. Also, the bidirectional relationships between the DOW JONES 100 and STANDARD&POORS 500 in-mean and in-variance have the same effect in both directions.     

Granger causality and path diagrams for multivariate time series

In this paper, we introduce path diagrams for multivariate time series which visualize the dynamic relationships among the variables. In these path diagrams, the vertices represent the components of the time series and are connected by arrows or lines according to the nonvanishing parameters in the autoregressive representation of the time series. We show that these path diagrams provide a framework for the analysis of the dependence structure of the time series. In particular, we give sufficient graphical conditions for Granger-noncausality and Granger-noncausality up to a certain horizon.     

Further evidence on causality and feedback across international stocks

This study extends the previous research on interdependence of international stock markets by using Geweke's (1982) causality test on seventeen stock market indices. The impact of the stock market crash of October 1987 on other national stock markets is investigated by disaggregating the data into pre- and post-crash periods. Direction of causality and feedback is studied using standard causality tests. The results indicate very few stock markets (namely, the U.K. and the U.S.A.) influence other markets significantly. Almost all markets react to other markets' past and present movements. Traditional major markets (Japan, France, and Canada) do not seem to be influential at all.     

Markov switching causality and the money–output relationship

The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time‐varying parameters. We model parameter time‐variation so as to reflect changes in Granger causality, and assume that these changes are stochastic and governed by an unobservable Markov chain. When applied to US data, our methodology allows us to reconcile previous puzzling differences in the outcome of conventional tests for money–output causality. Copyright © 2005 John Wiley & Sons, Ltd.     

Pierce and Haugh on characterizations of causality: A re-examination

This paper amends the set of equivalent necessary and sufficient conditions under which y does not cause x, provided by Pierce and Haugh (1977).     

Human capital inequality and income inequality: testing for causality

This study investigates the causal relationship between human capital inequality and income inequality in case of Pakistan. The annual time series data ranging from 1973 to 2009 is used for econometric analysis. Johanson co-integration and Granger Causality tests are used to confirm the existence of long run relationship and the causal relationship between human capital inequality and income inequality. The results indicate that there is positive relationship between the two types of inequalities in the long run. The estimates of causality test indicate that income inequality causes the human capital inequality but human capital inequality does not cause income inequality. Policy initiatives to reduce income inequality may empower people economically to avail skill building opportunities and accumulate their human capital through access to educational services.     

Testing for short- and long-run causality: A frequency-domain approach

The framework of Geweke (1982. Journal of the American Statistical Association 77, 304–324.) and Hosoya (1991. Probability Theory and Related Fields 88, 429–444.) is adopted to construct a simple test for causality in the frequency domain. This test can also be applied to cointegrated systems. To study the large sample properties of the test, we analyze the power against a sequence of local alternatives. The finite sample properties are investigated by means of Monte Carlo simulations. Our methodology is applied to investigate the predictive content of the yield spread for future output growth. Using quarterly US data we observe reasonable leading indicator properties at frequencies around one year and typical business cycle frequencies.     

An investigation of causality between insider transactions and stock returns

Prior research shows that corporate insiders engage in profitable transactions by trading securities of their own firms. The main purpose of this study is to examine whether insider transactions and stock returns have causality relationships at the firm level for a sample of 2,521 firms during the period 1988 to 1998. We find a large impact of stock returns on subsequent insider transactions at both the aggregate and firm levels. The impact appears to be negative which suggests that insiders buy after stock price decreases and sell after stock price increases. Our findings on the predictive content of insider transactions for subsequent stock returns are primarily consistent with prior literature. We observe a positive but weak relationship between insider transactions and future stock returns.     

The characterization of instantaneous causality: A correction

The purpose of this paper is to show that it is not possible to determine whether instantaneous causality exists between two time series by examining their contemporaneous cross-correlation.     

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。