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An application of dynamic factor models to nowcast regional economic activity in Spain

Author

Listed:
  • María Gil

    (Banco de España)

  • Danilo Leiva-Leon

    (Banco de España)

  • Javier J. Pérez

    (Banco de España)

  • Alberto Urtasun

    (Banco de España)

Abstract

The goal of this paper is to propose a model to produce nowcasts of GDP growth of Spanish regions, by means of dynamic factor models. This framework is capable to incorporate in a parsimonious way the relevant information available at the time that each forecast is made. We employ a Bayesian perspective to provide robust estimation of all the ingredients involved in the model. Accordingly, we introduce the Bayesian Factor model for Regions (BayFaR), which allows for the inclusion of missing data and combines quarterly data on regional real output growth (taken from the database of the AIReF and from the individual regional statistics institutes, when available) and monthly information associated to indicators of regional real activity. We apply the BayFaR to nowcast the GDP growth of the four largest regions of Spain, and illustrate the real-time nowcasting performance of the proposed framework for each case. We also apply the model to nowcast Spanish GDP in order to be able to assess the relative growth of each region.

Suggested Citation

  • María Gil & Danilo Leiva-Leon & Javier J. Pérez & Alberto Urtasun, 2019. "An application of dynamic factor models to nowcast regional economic activity in Spain," Occasional Papers 1904, Banco de España.
    • Handle: RePEc:bde:opaper:1904
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    References listed on IDEAS

    as
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    2. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    3. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    4. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    5. Henzel Steffen R. & Wohlrabe Klaus & Lehmann Robert, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    6. Gadea-Rivas, María Dolores & Gómez-Loscos, Ana & Leiva-Leon, Danilo, 2019. "Increasing linkages among European regions. The role of sectoral composition," Economic Modelling, Elsevier, vol. 80(C), pages 222-243.
    7. Allan, Grant & Koop, Gary & McIntyre, Stuart & Smith, Paul, 2014. "Nowcasting Scottish GDP Growth," SIRE Discussion Papers 2015-08, Scottish Institute for Research in Economics (SIRE).
    8. Concha Artola & María Gil & Javier J. Pérez & Alberto Urtasun & Alejandro Fiorito & Diego Vila, 2018. "Monitoring the Spanish economy from a regional perspective: main elements of analysis," Occasional Papers 1809, Banco de España.
    9. N/A, 2004. "Index for 2004," European Union Politics, , vol. 5(4), pages 511-512, December.
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    Citations

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    Cited by:

    1. Concha Artola & María Gil & Javier J. Pérez & Alberto Urtasun & Alejandro Fiorito & Diego Vila, 2018. "Monitoring the Spanish economy from a regional perspective: main elements of analysis," Occasional Papers 1809, Banco de España.
    2. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    3. Robert Lehmann & Sascha Möhrle, 2022. "Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data," CESifo Working Paper Series 9917, CESifo.
    4. Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
    5. Priscila Espinosa & Jose M. Pavía, 2023. "Automation in Regional Economic Synthetic Index Construction with Uncertainty Measurement," Forecasting, MDPI, vol. 5(2), pages 1-19, April.

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    More about this item

    Keywords

    regional activity; nowcasting; dynamic factor model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies

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