Forecasting the Estonian rate of inflation using factor models

Nicolas Reigl ()

No wp2016-8, Bank of Estonia Working Papers from Bank of Estonia

Abstract: 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

Keywords: Factor models; factor-augmented vector autoregressive models; factor analysis; principal components; inflation forecasting; forecast evaluation; Estonia (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 (search for similar items in EconPapers)
Date: 2016-10-10, Revised 2016-10-10
New Economics Papers: this item is included in nep-for and nep-mon
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