Assessing Generalized Method-of-Moments Estimates of the Federal Reserve Reaction Function

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Abstract
Estimating a forward-looking monetary policy rule by the generalized method of moments (GMM) has become a popular approach. We reexamine estimates of the Federal Reserve reaction function using several GMM estimators and a maximum likelihood (ML) estimator. First, we show that over the baseline period 1979–2000, these alternative approaches yield substantially different parameter estimates. Using Monte Carlo simulations, we show that the finite-sample GMM bias can account for only a small part of the discrepancy between estimates. We find that this discrepancy is more plausibly rationalized by the serial correlation of the policy shock, causing misspecification of GMM estimators through lack of instrument exogeneity. This correlation pattern is related to a shift in the reaction function parameters around 1987. Reestimating the reaction function over the 1987–2000 period produces GMM estimates that are very close to the ML estimate.