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Forecast accuracy of small and large scale dynamic factor models in developing economies
Author(s) -
LopezBuenache German
Publication year - 2018
Publication title -
review of development economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.531
H-Index - 50
eISSN - 1467-9361
pISSN - 1363-6669
DOI - 10.1111/rode.12392
Subject(s) - dynamic factor , econometrics , autoregressive model , factor analysis , constraint (computer aided design) , economics , scale (ratio) , series (stratigraphy) , computer science , mathematics , geography , paleontology , geometry , cartography , biology
Developing economies usually present limitations in the availability of economic data. This constraint may affect the capacity of dynamic factor models to summarize large amounts of information into latent factors that reflect macroeconomic performance. This paper addresses this issue by comparing the accuracy of two kinds of dynamic factor models at GDP forecasting for six Latin American countries. Each model is based on a dataset of different dimensions: a large dataset composed of series belonging to several macroeconomic categories (large scale dynamic factor model) and a small dataset with a few prescreened variables considered as the most representative ones (small scale dynamic factor model). Short‐term pseudo real time out‐of‐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 each country. Results (i) confirm the important role of the inclusion of latest released data in the forecast accuracy of both models, (ii) show better precision of predictions based on factors with respect to autoregressive models and (iii) identify the most adequate model for each country according to availability of the observed data.