Non-Causality in Bivariate Binary Time Series


In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger non-causality and simultaneous independence can be tested by Likelihood Ratio or Wald tests. A specialized version of the model, aimed at testing Granger non-causality with bivariate discrete-time survival data is also discussed. The proposed tests are illustrated in two empirical applications.

Dec 13, 2001 — Dec 15, 2001
12th (EC)2 (European Conferences of the Econom[etr]ics Community) “Causality and Exogeneity in Econometrics”
Louvain-la-Neuve, Belgium
Raffaello Seri
Raffaello Seri
Professor of Econometrics

My research interests include statistics, numerical analysis, operations research, psychology, economics and management.