Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/180105
Authors: 
Reigl, Nicolas
Year of Publication: 
2017
Citation: 
[Journal:] Baltic Journal of Economics [ISSN:] 2334-4385 [Volume:] 17 [Year:] 2017 [Issue:] 2 [Pages:] 152-189
Abstract: 
The paper presents forecasts of headline and core inflation in Estonia with factor models in a recursive pseudo out-of-sample framework. The factors are constructed with a principal component analysis and are then incorporated into vector autoregressive (VAR) forecasting models. The analyses show that certain factor-augmented VAR models improve upon a simple univariate autoregressive model but the forecasting gains are small and not systematic. Models with a small number of factors extracted from a large dataset are best suited for forecasting headline inflation. The results also show that models with a larger number of factors extracted from a small dataset outperform the benchmark model in the forecast of Estonian headline and, especially, core inflation.
Subjects: 
Factor models
factor-augmented vector autoregressive models
factor analysis
principal components
inflation forecasting
Estonia
JEL: 
C32
C38
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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