IDEAS home Printed from https://ideas.repec.org/a/spr/series/v3y2012i4p475-497.html
   My bibliography  Save this article

MICA-BBVA: a factor model of economic and financial indicators for short-term GDP forecasting

Author

Listed:
  • Máximo Camacho
  • Rafael Doménech

Abstract

In this paper we extend the Stock and Watson’s (Leading economic indicators, new approaches and forecasting records, 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. 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. Copyright The Author(s) 2012

Suggested Citation

  • Máximo Camacho & Rafael Doménech, 2012. "MICA-BBVA: a factor model of economic and financial indicators for short-term GDP forecasting," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 475-497, December.
    • Handle: RePEc:spr:series:v:3:y:2012:i:4:p:475-497
    DOI: 10.1007/s13209-011-0078-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s13209-011-0078-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s13209-011-0078-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Israel Sancho & maximo Camacho, 2002. "Spanish diffusion indexes," Computing in Economics and Finance 2002 276, Society for Computational Economics.
    3. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    4. 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.).
    5. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    6. 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.
    7. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non‐linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300, April.
    8. 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.
    9. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. 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.
    12. 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.
    13. Rafael Doménech & Víctor Gómez, 2005. "Ciclo económico y desempleo estructural en la economía española," Investigaciones Economicas, Fundación SEPI, vol. 29(2), pages 259-288, May.
    14. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    15. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    4. 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.
    5. Pablo Duarte & Bernd Süssmuth, 2014. "Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP," CESifo Working Paper Series 4574, CESifo.
    6. 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.
    7. 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.
    8. 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.
    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. 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.
    11. Gálvez-Soriano Oscar de Jesús, 2018. "Nowcasting Mexican GDP using Factor Models and Bridge Equations," Working Papers 2018-06, Banco de México.
    12. 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.
    13. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
    14. 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.
    15. 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 19285, Banco de la República, Economía Regional.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. William Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
    3. William A. Barnett & Marcelle Chauvet & Danilo Leiva-Leon, 2014. "Real-Time Nowcasting of Nominal GDP Under Structural Breaks," Staff Working Papers 14-39, Bank of Canada.
    4. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    5. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    6. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    7. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    8. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
    9. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    10. Antonello D’Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2016. "Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 569-594, Emerald Group Publishing Limited.
    11. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    12. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    13. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    14. 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.
    15. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    16. Peter Fuleky & Carl, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 2013-5, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    17. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    18. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 73-107.
    19. Evren Erdogan Cosar & Sevim Kosem & Cagri Sarikaya, 2013. "Do We Really Need Filters In Estimating Output Gap? : Evidence From Turkey," Working Papers 1333, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    20. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.

    More about this item

    Keywords

    Business cycles; Output growth; Short-term forecasting; E32; C22; E27;
    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:series:v:3:y:2012:i:4:p:475-497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.