Nowcasting Peruvian GDP using Leading Indicators and Bayesian Variable Selection

Fernando Pérez Forero ()

No 2018-010, Working Papers from Banco Central de Reserva del Perú

Abstract: There exists a large set of leading indicators that are directly related with GDP growth. However, it is often very difficult to select which of these indicators can be used in order to choose the best shortterm forecasting (nowcasting) model. In addition, it may be the case that more than one model can do this job accurately. Therefore, it would be convenient to average these potentially non-nested models. Following Scott and Varian (2015), we estimate a Structural State Space model through Gibbs Sampling and a spike-slab prior in order to perform the Stochastic Search Variable Selection (SSVS) method. Posterior simulations can be used to then compute the inclusion probability of each variable for the whole set of models considered. In-sample GDP estimates are very precise, taking into account the large set of regressors considered for the estimation. Data comes from the BCRPs database plus other additional sources.

Keywords: Nowcasting; Gibbs Sampling; Variable Selection; Model Averaging (search for similar items in EconPapers)
JEL-codes: E43 E51 E52 E58 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.bcrp.gob.pe/docs/Publicaciones/Documen ... trabajo-010-2018.pdf Application/pdf

Related works:
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:rbp:wpaper:2018-010

Access Statistics for this paper

More papers in Working Papers from Banco Central de Reserva del Perú Contact information at EDIRC.
Bibliographic data for series maintained by Research Unit ().