Forecasting the Estonian rate of inflation using factor models

Nicolas Reigl ()
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Nicolas Reigl: Department of Finance and Economics, Tallinn University of Technology, Tallinn, Estonia; Bank of Estonia, Tallinn, Estonia

Baltic Journal of Economics, 2017, vol. 17, issue 2, 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.

Keywords: Factor models; factor-augmented vector autoregressive models; factor analysis; principal components; inflation forecasting; Estonia (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:bic:journl:v:17:y:2017:i:2:p:152-189

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