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dc.contributor.authorJordà, Òscaren_US
dc.contributor.authorKozicki, Sharonen_US
dc.date.accessioned2011-12-15T12:57:15Z-
dc.date.available2011-12-15T12:57:15Z-
dc.date.issued2007en_US
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_US
dc.language.isoengen_US
dc.publisherBank of Canada Ottawaen_US
dc.relation.ispartofseriesBank of Canada Working Paper 2007,56en_US
dc.subject.jelC32en_US
dc.subject.jelE47en_US
dc.subject.jelC53en_US
dc.subject.ddc330en_US
dc.subject.keywordEconometric and statistical methodsen_US
dc.subject.stwNew-Keynesian Phillips Curveen_US
dc.subject.stwInferenzstatistiken_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleEstimation and inference by the method of projection minimum distanceen_US
dc.typeWorking Paperen_US
dc.identifier.ppn55560053Xen_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
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