Finite sample performance of small versus large scale dynamic factor models

Series: Working Papers. 1204.

Author: Rocio Alvarez, Maximo Camacho and Gabriel Perez-Quiros.

Topics: Quantitative methods | Non-financial corporations, businesses | Exchange rates | Economic growth and convergence | Household finances.

Published in: International Journal of Forecasting, 32 (3) July–September 2016, 680–694

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Finite sample performance of small versus large scale dynamic factor models (649 KB)

Abstract

We examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models out-perform large scale models in factor estimation and forecasting for high levels of cross-correlation across the idiosyncratic errors of series belonging to the same category, for oversampled categories and, especially, for high persistence in either the common factor series or the idiosyncratic errors. Using a panel of 147 US economic indicators, which are classified into 13 economic categories, we show that a small scale dynamic factor model that uses one representative indicator of each category yields satisfactory or even better forecasting results than a large scale dynamic factor model that uses all the economic indicators.