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ML vs GMM Estimates of Hybrid Macroeconomic Models (With an Application to the "New Phillips Curve")

  • Eric JONDEAU

    (Banque de France; ERUDITE, Université Paris 12 Val-de-Marne)

  • Hervé LE BIHAN

    (Banque de France)

Many macroeconomic models (including the NKPC - "New Keynesian" Phillips Curve) involve hybrid equations, in which some variables depend on both their lags and leads. Hybrid models have produced conflicting empirical results: GMM (respectively ML) estimation find the forward- looking component to be large (small). A rationalization for this conflict is provided, allowing for two kinds of misspecifications (omitted dynamics and measurement error): we show analytically in a simple DGP that the GMM (ML) estimator overstates (understates) the size of the forward-looking component. Monte-Carlo experiments indicate this result has some generality. We use these findings to rationalize discrepancies observed in NKPC estimates.

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Paper provided by EconWPA in its series Econometrics with number 0303004.

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Length: 43 pages
Date of creation: 05 Mar 2003
Date of revision:
Handle: RePEc:wpa:wuwpem:0303004
Note: Type of Document - Scientific Workplace; prepared on IBM PC ; to print on ; pages: 43; figures: included. This paper does not necessarily reflect the views of the Banque de France.
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