Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis

Pablo Duarte and Bernd Süßmuth

No 152, Working Papers from University of Leipzig, Faculty of Economics and Management Science

Abstract: Dynamic factor models based on Kalman Filter techniques are frequently used to nowcast GDP. This study deals with the selection of indicators for this practice. We propose a two-tiered mechanism which is shown in a case study to produce more accurate nowcasts than a benchmark stochastic process and a standard model including extreme bounds fragile indicators. Nowcasting accuracy nearly measures up to the one of real-time forecasts by an institution with an interest in high-quality nowcasts.

Keywords: dynamic factor; Kalman Filter; extreme bounds analysis (search for similar items in EconPapers)
JEL-codes: C38 C53 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/175347/1/1013890752.pdf (application/pdf)

Related works:
Journal Article: Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:leiwps:152

Access Statistics for this paper

More papers in Working Papers from University of Leipzig, Faculty of Economics and Management Science Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().