Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models
Nikolay Gospodinov (),
Raymond Kan () and
Cesare Robotti ()
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Raymond Kan: University of Toronto, Postal: Rotman School of Management, 105 St. George Street, Toronto, Ontario, Canada M5S 3E6, http://www.rotman.utoronto.ca/FacultyAndResearch/Faculty/FacultyBios/Kan.aspx
No 2015-9, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta
This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. Although the results for the maximum likelihood estimator are only applicable to linear asset-pricing models, the asymptotic distribution of the continuously updated GMM estimator is derived for general, possibly nonlinear, models. The large corrections in the asymptotic variances, which arise from explicitly incorporating model misspecification in the analysis, are illustrated using simulations and an empirical application.
Keywords: asset pricing; model misspecification; continuously updated GMM; maximum likelihood; asymptotic approximation; misspecification-robust tests (search for similar items in EconPapers)
JEL-codes: C12 C13 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models (2018)
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