Assessing Generalized Method of Moments Estimates of the Federal Reserve Reaction Function
Eric Jondeau, Hervé Le Bihan* and Clémentine Galles**
Journal of Business and Economic Statistics, 2004, 22(2), 225-239.
Abstract
Estimating a forward-looking monetary policy rule by the Generalized Method
of Moments (GMM) has become a popular approach since the influential papers
by Clarida, Gali, and Gertler (1998, 2000). We re-examine 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 only account
for 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 mis-specification of GMM estimators through lack of instrument exogeneity.
This correlation pattern is related to a shift in the reaction-function parameters
in 1987. Re-estimating the reaction function over the 1987-2000 period produces
GMM estimates which are very close to the ML estimate.
Keywords: Continuous-updating GMM; Finite-sample properties, Forward-looking
model, Maximum Likelihood estimator, Monetary policy reaction function.
JEL classification: E52, E58, F41.
* Banque de France, Centre de recherche
** GREMAQ, Université
des Sciences Sociales, Toulouse