Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/53877 
Kompletter Metadatensatz
Erscheint in der Sammlung:
DublinCore-FeldWertSprache
dc.contributor.authorJordà, Òscaren
dc.contributor.authorKozicki, Sharonen
dc.date.accessioned2011-12-15T12:57:15Z-
dc.date.available2011-12-15T12:57:15Z-
dc.date.issued2007-
dc.identifier.pidoi:10.34989/swp-2007-56en
dc.identifier.urihttp://hdl.handle.net/10419/53877-
dc.description.abstractA covariance-stationary vector of variables has a Wold representation whose coefficients can be semi-parametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of minimum distance to estimate model parameters. We call this estimator projection minimum distance (PMD) and show that its parameter estimates are consistent and asymptotically normal. In many cases, PMD is asymptotically equivalent to maximum likelihood estimation (MLE) and nests GMM as a special case. In fact, models whose ML estimation would require numerical routines (such as VARMA models) can often be estimated by simple least-squares routines and almost as efficiently by PMD. Because PMD imposes no constraints on the dynamics of the system, it is often consistent in many situations where alternative estimators would be inconsistent.We provide several Monte Carlo experiments and an empirical application in support of the new techniques introduced.en
dc.language.isoengen
dc.publisher|aBank of Canada |cOttawaen
dc.relation.ispartofseries|aBank of Canada Working Paper |x2007-56en
dc.subject.jelC32en
dc.subject.jelE47en
dc.subject.jelC53en
dc.subject.ddc330en
dc.subject.keywordEconometric and statistical methodsen
dc.subject.stwNew-Keynesian Phillips Curveen
dc.subject.stwInferenzstatistiken
dc.subject.stwSchätztheorieen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwTheorieen
dc.titleEstimation and inference by the method of projection minimum distance-
dc.typeWorking Paperen
dc.identifier.ppn55560053Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:bca:bocawp:07-56en

Datei(en):
Datei
Größe
718.39 kB





Publikationen in EconStor sind urheberrechtlich geschützt.