Résumé : The Granger Causality (GC) has been widely used in the field
of both theoretical and applied econometrics. The stationary processes
are the term of reference used in GC testing, which is generally
studied by means of a standard F test. However, a considerable
literature has proven an eminent presence of the unit root processes
(UR) in macroeconomic series. Then, considering the important
consecuences from spurious regression theory, it is important to
understand the behavior of GC test if we have this type of
nonstationary processes. In this work, we prove that, when the Data
Generating Process (DGP) of the series is UR with or without drift,
correct inference can not be drawn from an standard GC test and may
identify inexistent causal relationship in the Granger sense. To show
this we derive non-standard asymptotic distributions of F statistics
and we study the properties in finite samples using several Monte
Carlo experiments.

Cet exposé se tiendra en salle C20-13, 20ème étage, Université
Paris 1, Centre Pierre Mendès-France, 90 rue de Tolbiac, 75013 Paris
(métro : Olympiades).