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MICA-BBVA: A Factor Model of Economic and Financial Indicators for Short-term GDP Forecasting

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  • Maximo Camacho
  • Rafael Domenech

Abstract

In this paper we extend the Stock and Watson’s (1991) single-index dynamic factor model in an econometric framework that has the advantage of combining information from real and financial indicators published at different frequencies and delays with respect to the period to which they refer. We find that the common factor reflects the behavior of the Spanish business cycle well and helps to estimate with high precision the regime-switching probabilities in line with business cycle phases. We also show that financial indicators are useful for forecasting output growth, particularly when certain financial variables lead the common factor. Finally, we provide a simulated real-time exercise and prove that the model is a very useful tool for the short-term analysis of the Spanish Economy.

Suggested Citation

  • Maximo Camacho & Rafael Domenech, 2010. "MICA-BBVA: A Factor Model of Economic and Financial Indicators for Short-term GDP Forecasting," Working Papers 1021, BBVA Bank, Economic Research Department.
    • Handle: RePEc:bbv:wpaper:1021
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    3. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
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    12. Pérez-Quirós, Gabriel & Camacho, Máximo & Alvarez, Rocio, 2012. "Finite sample performance of small versus large scale dynamic factor models," CEPR Discussion Papers 8867, C.E.P.R. Discussion Papers.
    13. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    14. Maximo Camacho & Gabriel Perez Quiros, 2011. "Spain‐Sting: Spain Short‐Term Indicator Of Growth," Manchester School, University of Manchester, vol. 79(s1), pages 594-616, June.
    15. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Maximo Camacho & Jaime Martinez-Martin, 2014. "Real-time forecasting US GDP from small-scale factor models," Empirical Economics, Springer, vol. 47(1), pages 347-364, August.
    2. Nikolay P. Pilnik & Igor Pospelov & Ivan P. Stankevich, 2015. "Multiproduct Model Decomposition of Components of Russian GDP," HSE Working papers WP BRP 111/EC/2015, National Research University Higher School of Economics.
    3. Boriss Siliverstovs, 2016. "The franc shock and Swiss GDP: how long does it take to start feeling the pain?," Applied Economics, Taylor & Francis Journals, vol. 48(36), pages 3432-3441, August.
    4. Antonio José Orozco-Gallo & Pavel Vidal-Alejandro & Johana Sanabria-Domínguez & Jaime Andrés Collazos-Rodríguez, 2021. "Indicador coincidente de actividad económica en la recesión pandémica: el caso del Caribe colombiano," Documentos de trabajo sobre Economía Regional y Urbana 298, Banco de la Republica de Colombia.
    5. Gerson Javier Pérez-Valbuena & Diana Ricciulli-Marín & Jaime Bonet-Morón & Paula Barrios, 2021. "Reglas fiscales subnacionales en Colombia: desde su concepción hasta los resultados frente al COVID-19," Documentos de Trabajo Sobre Economía Regional y Urbana 19126, Banco de la República, Economía Regional.
    6. Pablo Duarte & Bernd Süssmuth, 2018. "Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 127-141, April.
    7. Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    8. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    9. López, Ana M., 2016. "El papel de la información económica como generador de conocimiento en el proceso de predicción: comparaciones empíricas del crecimiento del PIB regional /The Role of Economic Information as a Generat," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 34, pages 543-572, Agosto.
    10. Travis Berge & Òscar Jordà, 2013. "A chronology of turning points in economic activity: Spain, 1850–2011," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(1), pages 1-34, March.
    11. Ángel Cuevas & Enrique Quilis, 2012. "A factor analysis for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(3), pages 311-338, September.
    12. Gálvez-Soriano Oscar de Jesús, 2018. "Nowcasting Mexican GDP using Factor Models and Bridge Equations," Working Papers 2018-06, Banco de México.
    13. Pablo Duarte & Bernd Süssmuth, 2014. "Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP," CESifo Working Paper Series 4574, CESifo.
    14. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
    15. Guangbao Guo & Chunjie Wei & Guoqi Qian, 2023. "Sparse online principal component analysis for parameter estimation in factor model," Computational Statistics, Springer, vol. 38(2), pages 1095-1116, June.

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    More about this item

    Keywords

    dynamic factor model; GDP forecast; financial variables;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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