Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data

Maurício Franca Lila, Erick Meira and Fernando Luiz Cyrino Oliveira

Socio-Economic Planning Sciences, 2022, vol. 82, issue PB

Abstract: Hierarchical forecasting methods take advantage of the hierarchical structure of the data through base forecast reconciliation, generating results that are usually unbiased and more accurate than those provided by benchmark methods. When combining base forecasts through regression-based reconciliation strategies, however, some forecasts may behave like outliers, causing distortions to the reconciliation process. This work introduces the concept of robust estimation for hierarchical forecast reconciliation methods. We formalize two different robust-based approaches applied to unemployment data from multiple labor force surveys in Brazil. In doing so, we address a significant gap in the modelling and forecasting of unemployment, taking into account the hierarchical structure of the data. To demonstrate the potential and validity of the proposed approaches, we compare their performance with those from traditional and state-of-the-art methods. Overall, the robust approaches show promising forecasting results under multiple settings and through the lens of different evaluation metrics. Furthermore, the methodology developed herein is flexible, in the sense that it can be readily applied to other time series and deliver equally reliable results.

Keywords: Unemployment; Forecasting; Labor force survey; Hierarchical time series; Reconciliation methods; Robust estimation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0038012122000830
Full text for ScienceDirect subscribers only

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:eee:soceps:v:82:y:2022:i:pb:s0038012122000830

DOI: 10.1016/j.seps.2022.101298

Access Statistics for this article

Socio-Economic Planning Sciences is currently edited by Barnett R. Parker

More articles in Socio-Economic Planning Sciences from Elsevier
Bibliographic data for series maintained by Catherine Liu ().