Corona, Francisco González-Farías, Graciela Orraca, Pedro
Year of Publication:
[Journal:] Latin American Economic Review [ISSN:] 2196-436X [Volume:] 26 [Year:] 2017 [Issue:] 7 [Pages:] 1-35
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.
Dynamic factor models Common trends Factor-augmented vector autoregressive model Partial least squares Forecast error