Forecasting and nowcasting economic growth in the euro area using factor models

Many empirical studies provide evidence that factor models, which use a large data set of economic variables, can contribute to the computation of more accurate forecasts. In this study we examine the performance of four different factor models in a pseudo real-time forecasting competition for the euro area and five of its largest countries. Our aim is to empirically identify the best factor model approach for the forecasting and nowcasting of the quarterly Gross Domestic Product growth rate. We also propose some modifications of existing factor model specifications with the aim of empirically improving their forecast performances. We conclude that factor models consistently outperform the benchmark autoregressive model, before and during the crisis. Moreover, we find that the highest forecast accuracy is mostly produced by the collapsed dynamic factor model.

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Recommended citation: Hindrayanto, I., Koopman, S.J. and J.M. de Winter (2016), Forecasting and nowcasting economic growth in the euro area using factor models, International Journal of Forecasting 32(4), 1284-1305.