1. bookTom 38 (2022): Zeszyt 3 (September 2022)
Informacje o czasopiśmie
Pierwsze wydanie
01 Oct 2013
Częstotliwość wydawania
4 razy w roku
Otwarty dostęp

Timely Estimates of the Monthly Mexican Economic Activity

Data publikacji: 12 Sep 2022
Tom & Zeszyt: Tom 38 (2022) - Zeszyt 3 (September 2022)
Zakres stron: 733 - 765
Otrzymano: 01 Oct 2021
Przyjęty: 01 May 2022
Informacje o czasopiśmie
Pierwsze wydanie
01 Oct 2013
Częstotliwość wydawania
4 razy w roku

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. 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. 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. 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. 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

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