IDEAS home Printed from https://ideas.repec.org/p/bku/doctra/2014009.html
   My bibliography  Save this paper

Un indicador de la evolución del PIB uruguayo en tiempo real

Author

Listed:
  • Helena Rodríguez

    (Banco Central del Uruguay)

Abstract

Timely forecasts and assessment of economic activity are crucial for policy making. In Uruguay, the Gross Domestic Product (GDP) is published on a quarterly basis with a lag of about three months. In this paper we build a dynamic factor model to compute nowcasts and short-term forecasts of quarterly GDP exploiting the information available in various activity indicators that are published on a monthly basis. The predictive ability of the model yields better performance than other benchmark models for short-term forecasts. Moreover, forecasting errors decrease as new information becomes available.

Suggested Citation

  • Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    • Handle: RePEc:bku:doctra:2014009
    as

    Download full text from publisher

    File URL: https://www.bcu.gub.uy/Estadisticas-e-Indicadores/Documentos%20de%20Trabajo/9.2014.pdf
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of 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, pages 25-44, February.
    4. Peter A. Zadrozny, 1990. "Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals," Working Papers 90-5, Center for Economic Studies, U.S. Census Bureau.
    5. 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.
    6. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    7. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," Borradores de Economia 724, Banco de la Republica de Colombia.
    8. 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.
    9. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    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. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
    12. Liu, Philip & Matheson, Troy & Romeu, Rafael, 2012. "Real-time forecasts of economic activity for Latin American economies," Economic Modelling, Elsevier, vol. 29(4), pages 1090-1098.
    13. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    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. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    16. Huseyin Cagri Akkoyun & Mahmut Gunay, 2012. "Nowcasting Turkish GDP Growth," Working Papers 1233, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    17. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    18. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 276, European Central Bank.
    19. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. William A. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
    8. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    9. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    10. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    11. 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.
    12. Martínez-Martín, Jaime & Rusticelli, Elena, 2021. "Keeping track of global trade in real time," International Journal of Forecasting, Elsevier, vol. 37(1), pages 224-236.
    13. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    14. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    15. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    16. 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.
    17. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    18. Abel Rodríguez Tirado & Marcelo Delajara & Federico Hernández Álvarez, 2016. "Nowcasting Mexico’s Short-Term GDP Growth in Real-Time: A Factor Model versus Professional Forecasters," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2016), pages 167-182, October.
    19. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    20. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.

    More about this item

    Keywords

    real time forecasting; dynamic factor model; Uruguay;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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:bku:doctra:2014009. 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: Biblioteca Especializada (email available below). General contact details of provider: https://edirc.repec.org/data/bcugvuy.html .

    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.