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Can FAVAR improve Swedish inflation forecasting?

Berggren, Erik LU and Lodenius, Ellenor (2016) NEKN01 20161
Department of Economics
Abstract (Swedish)
The purpose of this thesis is to investigate whether factor augmented vectorautoregression (FAVAR) models estimated using principal component analysis are able to improve monthly inflation rate forecasts for Sweden. We produce 42 forecasts for the period January 2012 to June 2015 and evaluate the forecasts by their root mean square errors as well as their ability to correctly predict the sign of the inflation rate. The models forecasting performances are compared using the Diebold-Mariano test of equal predictive accuracy. Our results show that the investigated FAVAR models cannot significantly improve forecasts relative to a univariate model and that the FAVAR models perform worse with twelve lags than with only one lag.
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author
Berggren, Erik LU and Lodenius, Ellenor
supervisor
organization
course
NEKN01 20161
year
type
H1 - Master's Degree (One Year)
subject
keywords
Forecasting, factor models, principal component analysis, Phillips curve, Taylor rule
language
English
id
8877277
date added to LUP
2016-06-22 14:26:28
date last changed
2016-06-22 14:26:28
@misc{8877277,
  abstract     = {The purpose of this thesis is to investigate whether factor augmented vectorautoregression (FAVAR) models estimated using principal component analysis are able to improve monthly inflation rate forecasts for Sweden. We produce 42 forecasts for the period January 2012 to June 2015 and evaluate the forecasts by their root mean square errors as well as their ability to correctly predict the sign of the inflation rate. The models forecasting performances are compared using the Diebold-Mariano test of equal predictive accuracy. Our results show that the investigated FAVAR models cannot significantly improve forecasts relative to a univariate model and that the FAVAR models perform worse with twelve lags than with only one lag.},
  author       = {Berggren, Erik and Lodenius, Ellenor},
  keyword      = {Forecasting,factor models,principal component analysis,Phillips curve,Taylor rule},
  language     = {eng},
  note         = {Student Paper},
  title        = {Can FAVAR improve Swedish inflation forecasting?},
  year         = {2016},
}