8/2016 Nicolas Reigl. Forecasting the Estonian Rate of Inflation using Factor Models
Working Papers of Eesti Pank 8/2016
The paper presents forecasts of the 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 forecasting models. The analyses show that certain factor-augmented vector autoregressive 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. In contrast 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.
JEL classification: C32, C38, C53
Keywords: Factor models, factor-augmented vector autoregressive models, factor analysis, principal components, inflation forecasting, forecast evaluation, Estonia
The views expressed are those of the author and do not necessarily represent the official views of Eesti Pank or the Eurosystem.