Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195220
Authors: 
Fúquene, Jairo
Álvarez, Marta
Pericchi, Luis Raúl
Year of Publication: 
2015
Citation: 
[Journal:] Latin American Economic Review [ISSN:] 2196-436X [Volume:] 24 [Year:] 2015 [Issue:] 6 [Pages:] 1-17
Abstract: 
The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On the other hand, robust Bayesian dynamic models (RBDMs) do not assume a regular pattern or stability of the underlying system but can include points of statement breaks. In this paper we use RBDMs in order to account possible outliers and structural breaks in Latin-American economic time series. We work with important economic time series from Puerto Rico and Mexico. We show by using a random walk model how RBDMs can be applied for detecting historic changes in the economic inflation of Mexico. Also, we model the Consumer Price Index, the Economic Activity Index and the total number of employments economic time series in Puerto Rico using local linear trend and seasonal RBDMs with observational and states variances. The results illustrate how the model accounts the structural breaks for the historic recession periods in Puerto Rico.
Subjects: 
Robust Bayesian dynamic model
Outliers and structural breaks
Latin-American time series
Consumer Price Index
Economic Activity Index
Total number of employments
JEL: 
C11
C40
G17
N16
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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