[
Ahn, S. and A. Horenstein, 2013. “Eigenvalue ratio test for the number of factors.” Econometrica 81(3): 1203–1227. DOI: https://doi.org/10.3982/ECTA8968.
]Search in Google Scholar
[
Ali, U., Herbst, C.M., and C.A. Makridis. 2021. “The impact of COVID-19 on the US child care market: Evidence from stay-at-home orders.” Economics of Education Review 82: 102094. DOI: https://doi.org/10.1016/j.econedurev.2021.102094.
]Search in Google Scholar
[
Antolin-Diaz, J., Drechsel, T., and I. Petrella. 2017. “Tracking the slowdown in long-run GDP growth.” Review of Economics and Statistics 99(2): 343–356. DOI: https://doi.org/10.1162/REST_a_00646.
]Search in Google Scholar
[
Aprigliano, V., and L. Bencivelli. 2013. Ita-coin: a new coincident indicator for the Italian economy. Banca D’Italia. Working papers: 935: DOI: https://dx.doi.org/10.2139/ssrn.2405416v.10.2139/ssrn.2405416
]Search in Google Scholar
[
Bai, J. 2003. “Inferential theory for factor models of large dimensions.” Econometrica 71(1): 135–171. DOI: https://doi.org/10.1111/1468-0262.00392.
]Search in Google Scholar
[
Bai, J. 2004. “Estimating cross-section common stochastic trends in nonstationary panel data.” Journal of Econometrics 122(1): 137–183. DOI: https://doi.org/10.1016/j.jeconom.2003.10.022.
]Search in Google Scholar
[
Bai, J., and S. Ng. 2002. “Determining the number of factors in approximate factor models.” Econometrica 70(1): 191–221. DOI: https://doi.org/10.1111/1468-0262.00273.
]Search in Google Scholar
[
Bai, J., and S. Ng. 2004. “A PANIC attack on unit roots and cointegration.” Econometrica 72(4): 1127–1177. DOI: https://doi.org/10.1111/j.1468-0262.2004.00528.x.
]Search in Google Scholar
[
Bai, J., and S. Ng. 2007. “Determining the number of primitive shocks in factor models.” Journal of Business & Economic Statistics 25(1): 52–60. DOI: https://doi.org/10.1198/073500106000000413.
]Search in Google Scholar
[
Bai, J., and S. Ng. 2013. “Principal components estimation and identification of static factors.” Journal of Econometrics 176(1): 18–29. DOI: https://doi.org/10.1016Zj.jeconom.2013.03.007.10.1016/j.jeconom.2013.03.007
]Search in Google Scholar
[
Baríbura, M., Giannone, D., and L. Reichlin. 2011. “Nowcasting”, In Oxford Handbook of Economic Forecasting edited by Michael P. Clements and David F. Hendry: 193–224. DOI: https://doi.org/10.1093/oxfordhb/9780195398649.013.0008.
]Search in Google Scholar
[
Barigozzi, M., Lippi, M., and M. Luciani. 2015. Dynamic factor models, cointegration, and error correction mechanisms. arXiv preprint arXiv:1510.02399. DOI: https://doi.org/10.48550/arXiv.1510.02399.
]Search in Google Scholar
[
Barigozzi, M. Lippi, and M. Luciani. 2016. Non-Stationary Dynamic Factor Models for Large Datasets. SSRN 2741739. DOI: http://dx.doi.org/10.2139/ssrn.2741739.10.2139/ssrn.2741739
]Search in Google Scholar
[
Benchimol, J., S. Kazinnik, and Y. Saadon. 2021. “Federal Reserve communication and the COVID-19 pandemic.” Covid Economics 71: 218. DOI: https://cepr.org/content/-covid-economics-vetted-and-real-time-papers-0 (accessed January 2022).
]Search in Google Scholar
[
Boivin, J., and S. Ng. 2006. “Are more data always better for factor analysis?” Journal of Econometrics 132(1): 169–194. DOI: https://doi.org/10.1016/j.jeconom.2005.01.027.
]Search in Google Scholar
[
Buono, D., Mazzi, G., M. Marcellino, and G. Kapetanios. 2017. “Big data types for macroeconomic nowcasting.” Eurostat Review on National Accounts and Macroeconomic Indicators 1(2017): 93–145. Available at: https://ec.europa.eu/eurostat/-cros/content/big-data-types-macroeconomic-nowcasting-dario-buono-gian-luigi-mazzi-george-kapetanios_en (accessed October 2021).
]Search in Google Scholar
[
Caperna, G., Colagrossi, M., A. Geraci, and G. Mazzarella. 2022. “A Babel of web-searches: Googling unemployment during the pandemic.” Labour Economics (74). DOI: https://doi.org/10.1016/j.labeco.2021.102097.881971935153384
]Search in Google Scholar
[
Caruso, A. 2018. “Nowcasting with the help of foreign indicators: The case of Mexico.” Economic Modelling 69: 160–168. DOI: https://doi.org/10.1016Zj.econmod.2017.09.017.10.1016/j.econmod.2017.09.017
]Search in Google Scholar
[
Choi, H. and Varian. 2012. “Predicting the present with Google Trends.” Economic record 88: 2–9. DOI: https://doi.org/10.1111/j.1475-4932.2012.00809.x.
]Search in Google Scholar
[
Choi, I. 2017. “Efficient estimation of nonstationary factor models.” Journal of Statistical Planning and Inference 183: 18–43. DOI: https://doi.org/10.1016/j.jspi.2016.10.003.
]Search in Google Scholar
[
Corona, F., G. González-Farías, and P. Orraca. 2017a. “A dynamic factor model for the Mexican economy: Are common trends useful when predicting economic activity?” Latin American Economic Review 27(1). DOI: https://doi.org/10.1007/s40503-017-0044-7.
]Search in Google Scholar
[
Corona, F., P. Poncela, and E. Ruiz. 2017b. “Determining the number of factors after stationary univariate transformations.” Empirical Economics 53(1): 351–372. DOI: https://doi.org/10.1007/s00181-016-1158-5.
]Search in Google Scholar
[
Corona, F., P. Poncela, and E. Ruiz. 2020. “Estimating Non-stationary Common Factors: Implications for Risk Sharing.” Computational Economics 55(1): 37–60. DOI: https://doi.org/10.1007/s10614-018-9875-9.
]Search in Google Scholar
[
De Valk, S., D. de Mattos, and P. Ferreira. 2019. “Nowcasting: An R Package for Predicting Economic Variables Using Dynamic Factor Models.” The R Journal 11(1). DOI: https://doi.org/10.32614/RJ-2019-020.
]Search in Google Scholar
[
Delajara, M., F.H. Alvarez, and A.R. Tirado. 2016. “Nowcasting Mexico’s short-term GDP growth in real-time: A factor model versus professional forecasters.” Economia 17(1): 167–182. DOI: https://muse.jhu.edu/article/634035.
]Search in Google Scholar
[
Diebold, F. and R. Mariano, 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13(3): 253–263. DOI: https://doi.org/10.1080/07350015.1995.10524599.
]Search in Google Scholar
[
Doz, C., D. Giannone, and L. Reichlin. 2011. “A two-step estimator for large approximate dynamic factor models based on Kalman filtering.” Journal of Econometrics 164(1): 188–205. DOI: https://doi.org/10.1016/j.jeconom.2011.02.012.
]Search in Google Scholar
[
Doz, C., D. Giannone, and L. Reichlin. 2012. “A quasi maximum likelihood approach for large, approximate dynamic factor models” The Review of Economics and Statistics 94(4): 1014–1024. DOI: https://doi.org/10.1162/REST_a_00225.
]Search in Google Scholar
[
Forni, M., M. Hallin, M., Lippi, and L. Reichlin. 2000. “The generalized dynamic-factor model: identification and estimation.” Review of Economics and Statistics 82(4): 540–554. DOI: https://doi.org/10.1162/003465300559037.
]Search in Google Scholar
[
Galbraith, J. and G. Tkacz. 2018. “Nowcasting with payments system data.” International Journal of Forecasting 34(2): 366–376. DOI: https://doi.org/10.1016/j.ijforecast.2016.10.002.
]Search in Google Scholar
[
Gálvez-Soriano, O. 2020. “Nowcasting Mexico’s quarterly GDP using factor models and bridge equations.” Estudios Economicos 35(2): 213 –265. DOI: https://doi.org/10.24201/ee.v35i2.402.
]Search in Google Scholar
[
Gamboa, J.C.B. 2017. Deep learning for time-series analysis. arXiv:1701.01887. DOI: https://doi.org/10.48550/arXiv.1701.01887.
]Search in Google Scholar
[
Giannone, D., M. Lenza, and G.E. Primiceri. 2021. Economic predictions with big data: The illusion of sparsity. ECB Working Paper: 2021/2542, SSRN. DOI: http://dx.doi.org/10.2139/ssrn.3835164.10.2139/ssrn.3835164
]Search in Google Scholar
[
Giannone, D., L. Reichlin., and D. Small. 2008. “Nowcasting: The real-time informational content of macroeconomic data”. Journal of Monetary Economics 55(4): 665–676. DOI: https://doi.org/10.1016/j.jmoneco.2008.05.010.
]Search in Google Scholar
[
Goldsmith-Pinkham, P. and A. Sojourner. 2020. “Predicting Initial Unemployment Insurance Claims Using Google Trends.” Technical report, Working Paper. DOI: https://paulgp.github.io/GoogleTrendsUINowcast/google_trends_UI.html.
]Search in Google Scholar
[
González-Astudillo, M. and D. Baquero. 2019. “A nowcasting model for Ecuador: Implementing a time-varying mean output growth.” Economic Modelling 82: 250–263. DOI: https://doi.org/10.1016/j.econmod.2019.01.010.
]Search in Google Scholar
[
Graff, M., D. Moctezuma, S. Miranda-Jiménez, S., E.S. Tellez. 2022. “A Python library for exploratory data analysis and knowledge discovery on Twitter data.” Computers & Geosciences, 159: 105012. DOI: https://doi.org/10.1016Zj.cageo.2021.105012.10.1016/j.cageo.2021.105012
]Search in Google Scholar
[
Guerrero, V.M., A.C. García, A. C., and E. Sainz. 2013. “Rapid Estimates of Mexico’s Quarterly GDP.” Journal of Official Statistics 29(3): 397–423. DOI: https://doi.org/10.2478/jos-2013-0033.
]Search in Google Scholar
[
Harvey, A., and G. Phillips. 1979. “Maximum Likelihood Estimation of Regression Models With Autoregressive-Moving Averages Disturbances.” Biometrika 152: 49–58. DOI: https://doi.org/10.1093/biomet/66.1.49.
]Search in Google Scholar
[
Hewamalage, H., C. Bergmeir, and K. Bandara. 2021. “Recurrent Neural Networks for Time Series Forecasting: Current status and future directions.” International Journal of Forecasting: 37(1): 388–427. DOI: https://doi.org/10.1016/j.ijforecast.2020.06.008.
]Search in Google Scholar
[
Huang, G.B., Q.Y. Zhu, and C.K. Siew. 2006. “Extreme learning machine: Theory and applications.” Neurocomputing 70(1–3): 489–501. DOI: https://doi.org/10.1016/j.neucom.2005.12.126.
]Search in Google Scholar
[
INEGI. a. Indicador Global de la Actividad Económica. Available at: https://www.inegi.org.mx/temas/igae/
]Search in Google Scholar
[
INEGI. b. Estimación Oportuna del PIB Trimestral. Available at: https://www.inegi.org.mx/temas/pibo/.
]Search in Google Scholar
[
INEGI. c. Indicador Oportuno de la Actividad Económica. https://www.inegi.org.mx/investigacion/ioae/
]Search in Google Scholar
[
INEGI. d. Indicador Mensual Oportuno de la Actividad Manufacturera. Available at: https://www.inegi.org.mx/investigacion/imoam/
]Search in Google Scholar
[
Kourentzes, N., Barrow, D.K., and S.F. Crone. 2014. “Neural network ensemble operators for time series forecasting.” Expert Systems with Applications 41(9): 4235–4244. DOI: https://doi.org/10.1016/j.eswa.2013.12.011.
]Search in Google Scholar
[
León, C., and F. Ortega. 2018. “Nowcasting economic activity with electronic payments data: A predictive modeling approach.” Revista de economia del Rosario 21(2): 381–407. DOI: https://dialnet.unirioja.es/servlet/articulo?codigo=7411408.
]Search in Google Scholar
[
Onatski, A. 2010. “Determining the number of factors from empirical distribution of eigenvalues.” The Review of Economics and Statistics: 92(4): 1004–1016. DOI: https://doi.org/10.1162/REST_a_00043.
]Search in Google Scholar
[
Ord, J., R. Fildes, and N. Kourentzes. 2017. Principles of Business Forecasting-2nd Ed. Wessex, Incorporated.
]Search in Google Scholar
[
Poncela, P. and E. Ruiz. 2016. “Small versus big data factor extraction in Dynamic Factor Models: An empirical assessment in dynamic factor models.” In Advances in Econometrics, edited by E. Hillebrand, and S.J. Koopman. 35: 401–434. DOI: https://doi.org/10.1108/S0731-905320150000035010.
]Search in Google Scholar
[
Sezer, O.B., M.U. Gudelek, and A.M. Ozbayoglu. 2020. “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019.” Applied Soft Computing 90: 106–181. DOI: https://doi.org/10.1016/j.asoc.2020.106181.
]Search in Google Scholar
[
Shmueli, G. 2010. “To explain or to predict?” Statistical science 25(3): 289–310. DOI: https://doi.org/10.1214/10-STS330.
]Search in Google Scholar
[
Stephens-Davidowitz, S. and H. Varian. 2014. A hands-on guide to Google data. Technical report, Google Inc. Available at: https://people.ischool.berkeley.edu/,hal/Papers/2015/primer.pdf (accessed October 2021).
]Search in Google Scholar
[
Stock, J.H., and M.V. Watson. 2011. “Dynamic factor models.” In Oxford Handbook of Economic Forecasting. edited by M.P. Clements, and D.F. Hendry, Oxford: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195398649.013.0003.
]Search in Google Scholar
[
Tibshirani, R. 1996. “Regression shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 58(1): 267 – 288. DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
]Search in Google Scholar
[
Varian, H.R. 2014. “Big data: New tricks for econometrics.” Journal of Economic Perspectives 28(2): 3–28. DOI: https://doi.org/10.1257/jep.28.2.3.
]Search in Google Scholar
[
World Bank, World Bank national accounts data. Available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
]Search in Google Scholar