  In this paper, we present a new approach based on dynamic factor models
(DFMs) to perform nowcasts for the percentage annual variation of the Mexican
Global Economic Activity Indicator (IGAE in Spanish). The procedure consists of
the following steps: i) build a timely and correlated database by using
economic and financial time series and real-time variables such as social
mobility and significant topics extracted by Google Trends; ii) estimate the
common factors using the two-step methodology of Doz et al. (2011); iii) use
the common factors in univariate time-series models for test data; and iv)
according to the best results obtained in the previous step, combine the
statistically equal better nowcasts (Diebold-Mariano test) to generate the
current nowcasts. We obtain timely and accurate nowcasts for the IGAE,
including those for the current phase of drastic drops in the economy related
to COVID-19 sanitary measures. Additionally, the approach allows us to
disentangle the key variables in the DFM by estimating the confidence interval
for both the factor loadings and the factor estimates. This approach can be
used in official statistics to obtain preliminary estimates for IGAE up to 50
days before the official results.
