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Forecasting key macroeconomic variables from a large number of predictors: a state space approach
Author(s) -
Raknerud Arvid,
Skjerpen Terje,
Swensen Anders Rygh
Publication year - 2010
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1131
Subject(s) - dynamic factor , univariate , benchmark (surveying) , state variable , econometrics , key (lock) , state space representation , state space , computer science , variable (mathematics) , factor analysis , factor (programming language) , set (abstract data type) , statistics , mathematics , multivariate statistics , algorithm , machine learning , physics , computer security , geodesy , thermodynamics , mathematical analysis , programming language , geography
We use state space methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2–2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables. Copyright © 2009 John Wiley & Sons, Ltd.