Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195243
Authors: 
Corona, Francisco
González-Farías, Graciela
Orraca, Pedro
Year of Publication: 
2017
Citation: 
[Journal:] Latin American Economic Review [ISSN:] 2196-436X [Volume:] 26 [Year:] 2017 [Issue:] 7 [Pages:] 1-35
Abstract: 
In this paper we propose to use the common trends of the Mexican economy in order to predict economic activity one and two steps ahead. We exploit the cointegration properties of the macroeconomic time series, such that, when the series are I(1) and cointegrated, there is a factor representation, where the common factors are the common trends of the macroeconomic variables. Thus, we estimate a large non-stationary dynamic factor model using principal components (PC) as suggested by Bai (J Econom 122(1):137-183, 2004), where the estimated common factors are used in a factor-augmented vector autoregressive model to forecast the Global Index of Economic Activity. Additionally, we estimate the common trends through partial least squares. The results indicate that the common trends are useful to predict Mexican economic activity, and reduce the forecast error with respect to benchmark models, mainly when estimated using PC.
Subjects: 
Dynamic factor models
Common trends
Factor-augmented vector autoregressive model
Partial least squares
Forecast error
JEL: 
C38
C53
E00
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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