Aggregating Rational Expectations Models in the Presence of Unobserved Micro Heterogeneity
Our paper addresses the correction of the aggregation bias in linear rational expectations models when there is some unobserved micro-parameter heterogeneity and only macro data are available. Starting from Lewbel (1994), we propose two new consistent estimators, which rely on a flexible parametric specification of the cross-sectional parameter distributions and account for the dependence across coeffcients inherent in such models. A Monte-Carlo study reveals that the finite-sample and asymptotic properties of the proposed estimators correct the aggregation bias found with the maximum-likelihood and generalized-method-of-moments approaches. As a by-product, we can also infer the cross-sectional distribution of the parameters. Finally, we reassess the empirical evidence about the New Keynesian Phillips curve and explain the apparent discrepancy between micro- and macro-based estimates of the average persistence of inflation.
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