Forecasting key macroeconomic variables from a large number of predictors: A state space approach
Terje Skjerpen and Anders Rygh Swensen
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.
Dynamic factor model; Forecasting; State space; AR models
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