Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31347
Authors: 
Jordà, Òscar
Year of Publication: 
2007
Series/Report no.: 
Working papers // University of California, Department of Economics 07,8
Abstract: 
A covariance-stationary vector of variables has a Wold representation whose coefficients can be semiparametrically 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: 
impulse response
local projection
minimum chi-square
minimum distance.
JEL: 
C32
E47
C53
Document Type: 
Working Paper

Files in This Item:
File
Size
678.21 kB





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