Forecasting the Estonian rate of inflation using factor models
Forecasting the Estonian rate of inflation using factor models
Author(s): Nicolas ReiglSubject(s): Economy, Methodology and research technology, Financial Markets
Published by: BICEPS/SSE Riga
Keywords: models; factor-augmented vector autoregressive models; factor analysis; principal components; inflation forecasting; Estonia;
Summary/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.
Journal: Baltic Journal of Economics
- Issue Year: 17/2017
- Issue No: 2
- Page Range: 152-189
- Page Count: 38
- Language: English