Footnote:
In: Applied Economics Letters, Forthcoming
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 5, 2019 erstellt
Description:
A number of recent studies in the macro-finance literature that addresses the link between asset prices and economic fluctuations have focused on the usefulness of various factor models in the context of now-casting using very big dataset. The issue of factor extraction is usually swept under the carpet in the factor model literature, where it seems that all that is needed is a large number of economic and financial variables. We contribute to this literature by analyzing whether factor estimation methods matters for the performance of factor-based now-casting models based on selected emerging markets GDP. Ancillary findings based on our GDP now-casting experiments on major emerging market countries underscore the advantage of sparse principal component analysis based factor estimation approach. These results show that imposing a sparse structure on the whole dataset is generally a useful step towards reducing the forecast errors in the context of GDP now-casting model specification