Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies

Zusammenfassung

This paper compares forecast accuracy of two Dynamic Factor Models in a context of constraints in terms of data availability. Estimation technique and properties of the factor decomposition depend on the cross section dimension of the dataset included in each model: a large dataset composed by series belonging to seven broad categories or a small dataset with a few prescreened variables. Short term outof-sample forecast of GDP growth is carried out with both models reproducing the real time situation of data accessibility derived from the publication lags of the series in six Latin American countries.Results show i) the important role of the inclusion of latest released data in the forecast accuracy of both models, ii) the better precision of predictions based on factors with respect to autoregressive models and iii) identify the most adequate model for each of these six countries in different temporal horizons.