EconStor >
Bank of Canada, Ottawa >
Bank of Canada Working Papers >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/53877
  
Title:Estimation and inference by the method of projection minimum distance PDF Logo
Authors:Jordà, Òscar
Kozicki, Sharon
Issue Date:2007
Series/Report no.:Bank of Canada Working Paper 2007,56
Abstract:A 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.
Subjects:Econometric and statistical methods
JEL:C32
E47
C53
Document Type:Working Paper
Appears in Collections:Bank of Canada Working Papers

Files in This Item:
File Description SizeFormat
55560053X.pdf718.39 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/53877

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.