Forecasting the Estonian rate of inflation using factor models

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Baltic Journal of Economics ; ISSN: 2334-4385 ; Volume: 17 ; Year: 2017 ; Issue: 2 ; Pages: 152-189 ; London: Taylor & Francis

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Forecasting Models; Simulation Methods
• Subject: Factor models ; factor-augmented vector autoregressive models ; factor analysis ; principal components ; inflation forecasting ; Estonia

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Reigl, Nicolas
• Event: Veröffentlichung
• (who): Taylor & Francis
• (where): London
• (when): 2017

Further information

• DOI: doi:10.1080/1406099X.2017.1371976
• Handle: http://hdl.handle.net/10419/180105
• Last update: 10.03.2025, 11:42 AM CET