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The estimation of factors in FAVAR models

Berggren, Erik LU (2017) NEKP01 20171
Department of Economics
Abstract (Swedish)
The use of factor-augmented vector autoregression (FAVAR) models has become increasingly popular in the literature of empirical macroeconomics. This paper sheds light on the different factor estimation methods that are available to researchers. More specifically, this paper examines the widely used principal component method but also the computationally simpler common correlated effects method as well as the more advanced likelihood-based method using the Gibbs sampler. The results indicate very little difference between the principal component method and the common correlated effects method, which can facilitate the estimation of FAVAR models for researchers within the field of macroeconomics.
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author
Berggren, Erik LU
supervisor
organization
course
NEKP01 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Common correlated effects, principal component analysis, Gibbs sampling, impulse response, factor models
language
English
id
8909902
date added to LUP
2017-07-10 14:35:01
date last changed
2017-07-10 14:35:01
@misc{8909902,
  abstract     = {The use of factor-augmented vector autoregression (FAVAR) models has become increasingly popular in the literature of empirical macroeconomics. This paper sheds light on the different factor estimation methods that are available to researchers. More specifically, this paper examines the widely used principal component method but also the computationally simpler common correlated effects method as well as the more advanced likelihood-based method using the Gibbs sampler. The results indicate very little difference between the principal component method and the common correlated effects method, which can facilitate the estimation of FAVAR models for researchers within the field of macroeconomics.},
  author       = {Berggren, Erik},
  keyword      = {Common correlated effects,principal component analysis,Gibbs sampling,impulse response,factor models},
  language     = {eng},
  note         = {Student Paper},
  title        = {The estimation of factors in FAVAR models},
  year         = {2017},
}