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.
Highlights
In recent years, due to the availability of data on a vast number of correlated macroeconomic and financial variables collected regularly by statistical agencies, there has been an increasing interest in modeling large systems of economic time series
In this paper we estimated the common trends of the Mexican economy using a large dataset of macroeconomic variables
We estimated the common trends using I(1) variables through principal components (PC), determining the number of common factors according to the Onatski (2010) and the Ahn and Horenstein (2013) procedures
Summary
Due to the availability of data on a vast number of correlated macroeconomic and financial variables collected regularly by statistical agencies, there has been an increasing interest in modeling large systems of economic time series. DFMs were introduced by Geweke (1977) and Sargent and Sims (1977) with the aim of representing the dynamics of large systems of time series through a small number of hidden common factors, and are mainly used for one of the following two objectives: first, forecasting macroeconomic variables and second, estimating the underlying factor in order to carry out policy-making (e.g., the business cycle; lagging, coincident, and leading indicators; or impulse-response functions, among other aspects) Another interesting application is to use the common factors as instrumental variables or exogenous regressors in panel data analysis. It is important to comment that, in a similar way as we have proposed in this study but in a stationary framework, Brauning and Koopman (2014) propose to use collapsed dynamic factor analysis in order to predict target variables of the economy, exploiting the state-space representation between the target variables and the common factors They conclude that the forecast accuracy is improved with respect to benchmark models.
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