Mesure des Associations Phénoménales et Causalité en Macro-Économétrie

Bibliographic Data

ID3101279
AuthorsJean-François Jacques (0000-0002-0076-9229, corresponding author), Emmanuel Picavet (0000-0001-7966-7905, corresponding author)
Year2003
Volume42
Issue4
Pages591-624
Publication date2003-12-01
Open AccessYes
TypeARTICLE
VenueSocial Science Information (JOURNAL)
Journal identifiersISSN: 0539-0184 • E-ISSN: 1461-7412
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/0539018403424007
OpenAlexW2153268963
LanguageFR
References cited23

The article studies the conceptions of causality encountered in macroeconometrics. From an empiricist standpoint, it is natural to privilege measurements of phenomenal associations, spread over a period of time, between series of data. This method has made a major comeback since the 1970s, whereas these years seem to be moving away from the established approach to research in causality in this area. The authors seek to establish in particular the type of causality underpinning the approaches put forward by Clive Granger and Christopher Sims. They show that this definition of causality has distinctive advantages for the study of phenomena in which forecasting, different sets of information, interaction between the behaviour of developers of modelization tools and that of the other agents play a determining role

Econometrics · Economics · Empiricism · Epistemology · Granger causality · Physics · Positive economics · Sociology · Underpinning · Complex Systems and Time Series Analysis · Computer Science · Engineering · History · Market Dynamics and Volatility · Monetary Policy and Economic Impact · Philosophy

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