Abstract
ABSTRACTThe 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.
Highlights
Inflation and changes in inflation are key measures of macroeconomic performance, so it follows that forecasting inflation is important in countries around the world, including Estonia
The correlation between the observed variables and the unobserved common component can be analysed by extracting the variables that are most characteristic for each dimension obtained by principal component analysis
This paper investigates the performance of factor-augmented vector autoregressive (VAR) models when they are used to predict the Estonian headline and core inflation rates
Summary
Inflation and changes in inflation are key measures of macroeconomic performance, so it follows that forecasting inflation is important in countries around the world, including Estonia. Forecasters earlier relied on models with only a few predictors, until increasing amounts of data became available at high levels of sectoral, regional and temporal disaggregation Those macroeconomic, microeconomic and financial time series hold information that may be useful for economic forecasting and empirical analysis of monetary policy (Ibarra-Ramírez 2010). Factor models are flexible in the way that they can simultaneously accommodate data released at different times, frequencies and areas Their methods for extracting driving factors are statistically rigorous, as they are agnostic about the structure of the economy (Bernanke & Boivin 2003). 2 It is the first systematic study to analyse the applicability of a factor-augmented vector autoregressive (VAR) model for forecasting inflation in Estonia It examines the importance of the number of factors in the inflation forecasting model, when the factors are extracted from datasets where consumer price indicators are excluded or from subgroups of variables. The Online Appendix displays the data used in the benchmark model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
