Convergence Properties of the Likelihood of Computed Dynamic Models.
This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function...
| Publicado en: | Econometrica Vol. 74; no. 1; pp. 93 - 120 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
January 2006
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511286154&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511286154 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: N pubinfo: dt: January 2006 vid: 74 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 511286154 10.1111/j.1468-0262.2006.00650.x ppf: 93 ppct: 27 formats: tig: atl: Convergence Properties of the Likelihood of Computed Dynamic Models. aug: au: Fernández-Villaverde, Jesús Rubio-Ramírez, Juan F. Santos, Manuel S. su: Probability theory Economic convergence sug: subj: Probability theory Economic convergence ab: This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods? First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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