Optimal inference in dynamic models with conditional moment restrictions

Publikation: Working paperForskning

  • Institut for Økonomi
By an application of the theory of optimal estimating function, optimal in-
struments for dynamic models with conditional moment restrictions are derived.
The general efficiency bound is provided, along with estimators attaining the
bound. It is demonstrated that the optimal estimators are always at least as ef-
ficient as the traditional optimal generalized method of moments estimator, and
usually more efficient. The form of our optimal instruments resembles that from
Newey (1990), but involves conditioning on the history of the stochastic pro-
cess. In the special case of i.i.d. observations, our optimal estimator reduces to
Newey's. Specification and hypothesis testing in our framework are introduced.
We derive the theory of optimal instruments and the associated asymptotic dis-
tribution theory for general cases including non-martingale estimating functions
and general history dependence. Examples involving time-varying conditional
volatility and stochastic volatility are offered.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider39
StatusUdgivet - 2008

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