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Oral Communications<br />
CO6<br />
Thursday, September 5th<br />
18:00<br />
On the robustness of the general dynamic<br />
factor model with infinite-dimensional space:<br />
identification, estimation and forecasting<br />
Luiz Koodi Hotta<br />
IMECC/Unicamp<br />
Carlos Trucíos<br />
Pedro L. Valls Pereira<br />
João Henrique Gon¸calves Mazzeu<br />
Marc Hallin<br />
General dynamic factor models (GDFM) have shown to be a promising tool to circumvent the<br />
curse of dimensionality in time series and have been successfully applied in many economic and<br />
financial applications. However, their performance in the presence of outliers has not been analyzed<br />
yet. In this paper, we study the impact of additive outliers on the identification, estimation and<br />
forecasting performance of GDFM. Based on our findings, we propose a robustified version of the<br />
procedures introduced by Hallin and Liska (2007, JASA 102(478): 603-617), Forni et al. (2015, J<br />
Econom 185(2): 359-373) and Forni et al. (2017, J Econom 199(1): 74-92). Our proposal is<br />
evaluated via Monte Carlo experiments and in empirical data.<br />
Keywords: Dimension reduction; Large dimension volatility; Large panels<br />
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