Limited-information methods are commonly used to estimate forward-looking models with rational expectations, such as the "New Keynesian Phillips Curve" of Galf and Gertler (1999). In this paper, we address issues of identification that have been overlooked due to the incompleteness of the...

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be...

Limited-information identification-robust methods on the indexation and price rigidity parameters of the New Keynesian Phillips Curve yield very wide confidence intervals. Full-information methods impose more restrictions on the reduced-form dynamics and thus make more efficient use of the...

We re-examine the evidence on the new Phillips curve model of Gali and Gertler (Journal of Monetary Economics 1999) using the conditional score test of Kleibergen (Econometrica 2005), which is robust to weak identification. In contrast to earlier studies, we find that US postwar data are...

Recently, single-equation estimation by the generalized method of moments (GMM) has become popular in the monetary economics literature, for estimating forward-looking models with rational expectations. We discuss a method for analysing the empirical identification of such models that exploits...

We consider a prototypical representative-agent forward-looking model, and study the low frequency variability of the data when the agent's beliefs about the model are updated through linear learning algorithms. We find that learning in this context can generate strong persistence. The degree of...

We consider a prototypical representative-agent forward-looking model, and study the low frequency variability of the data when the agent's beliefs about the model are updated through linear learning algorithms. We nd that learning in this context can generate strong persistence. The degree of...