'Identification in Adaptive Learning Models'
Chevillon et al. (2010) show that, under certain conditions, the structural parameters may not all be identiﬁed as the equilibrium under learning converges to that of RE. Clearly this raises concerns about the reliability of the estimates in models that assume E-stability. However, as we show in this paper with the aid of a simple bivariate example, identiﬁcation of the structural parameters can be improved as long as one of the variables is inﬂuenced by expectations of other variables, but is itself not directly or indirectly part of the expectational feedback loop. As it turns out, the interest rate, speciﬁed by the Taylor (1993) rule in the NK-DSGE models, satisﬁes precisely this condition. Note that expectations of interest rate appear neither in the Taylor rule (direct), nor in the IS or Phillips curve equations (indirect). Intuitively, the interest rate provides an additional measurement that aids in the identiﬁability of the parameters by shrinking the set of possible parameter values that give rise to the same data.
Joint with Srikanth Ramamurthy (Loyola University).
Contact person: Florian Sniekers
Conference room JK 2.50