Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/31347 
Autor:innen: 
Erscheinungsjahr: 
2007
Schriftenreihe/Nr.: 
Working Paper No. 07-8
Verlag: 
University of California, Department of Economics, Davis, CA
Zusammenfassung: 
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.
Schlagwörter: 
impulse response
local projection
minimum chi-square
minimum distance.
JEL: 
C32
E47
C53
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
678.21 kB





Publikationen in EconStor sind urheberrechtlich geschützt.