Francisco Corona, Graciela González-Farías, Jesús López-Pérez
arXiv 25 Jan 2021 · Statistics — Applications · 2 citations (OpenAlex)
arXiv:2101.10383 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Corona, F., Poncela, P., and Ruiz, E (2020) Estimating Non-stationary Common Factors: Implications for Risk Sharing self | 1.000 | 6 | 4 | 100% |
| 2 | Doz, C., Giannone, D., and Reichlin, L (2011) A two-step estimator for large approximate dynamic factor models based on Kalman filtering | 1.000 | 6 | 3 | 100% |
| 3 | Corona, F., González-Farías, G., and Orraca, P (2017) A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity? self | 0.928 | 4 | 3 | 100% |
| 4 | Gálvez-Soriano, O (2020) Nowcasting Mexico's quarterly GDP using factor models and bridge equations | 0.928 | 4 | 3 | 100% |
| 5 | Giannone, D., Reichlin, L., and Small, D (2008) Nowcasting: The real-time informational content of macroeconomic data | 0.874 | 7 | 2 | 100% |
| 6 | Onatski, A (2010) Determining the number of factors from empirical distribution of eigenvalues | 0.811 | 4 | 2 | 100% |
| 7 | Bai, J. and Ng, S (2004) A PANIC attack on unit roots and cointegration | 0.737 | 3 | 2 | 100% |
| 8 | Boivin, J. and Ng, S (2006) Are more data always better for factor analysis? | 0.644 | 2 | 2 | 100% |
| 9 | Poncela, P. and Ruiz, E (2016) Small versus big data factor extraction in Dynamic Factor Models: An empirical assessment in dynamic factor models, in Hillebran… | 0.644 | 2 | 2 | 100% |
| 10 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.511 | 2 | 1 | 100% |
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