z-logo
open-access-imgOpen Access
FAVAR (Factor-Augmented Vector Autoregression) Modeli Literatür Taraması
Author(s) -
Bige Küçükefe,
Dündar Murat DEMİRÖZ
Publication year - 2017
Publication title -
fiscaoeconomia
Language(s) - English
Resource type - Journals
ISSN - 2564-7504
DOI - 10.25295/fsecon.295547
Subject(s) - vector autoregression , autoregressive model , impulse response , econometrics , bayesian vector autoregression , computer science , mathematics , statistics , mathematical analysis , bayesian probability
In the Vector Autoregressive (VAR) models, which are widely used in economic studies and developed by Sims (1980), impulse response functions can only be obtained from variables included only because of the infrequent use of information sets, and the dimensions of structural shocks can not be measured precisely. It is also not possible that for some variables to be represented by a single time series. The VAR estimation is insufficient for parsing operations involving large data sets. FAVAR (Factor Augmented Vector Autoregression) method was developed by Bernanke, Boivin and Eliasz (2005) and this method can use large data sets. In this study, FAVAR method is tried to be explained by comparing with VAR, and a literature search is being conducted in this subject.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom