IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1073.html
   My bibliography  Save this paper

Forecasting the Colombian Unemployment Rate Using Labour Force Flows

Author

Listed:
  • Francisco Lasso-Valderrama

    (Banco de la República de Colombia)

  • Héctor M. Zárate-Solano

    (Banco de la República de Colombia)

Abstract

Accurate predictions of future magnitudes of the unemployment rate are crucial for monetary policy. This paper investigates whether the use of disaggregated household survey data improves the forecasts of the Colombian 13 cities unemployment rate. We conduct an outof-sample forecast exercise to compare the performance of a model that incorporates flows of workers across different states of the labour market to that of various macroeconomic non-structural models. The paper follows the approach proposed by Barnichon & Nekarda (2013). Our results indicate that the two-state-flow model provides substantially better forecasts of the unemployment rate over longer horizons (more than five months ahead). Additionally, when forecasts are combined, significant gains in every forecasting horizon occurs. This combined forecast shows a 23% reduction in overall RMSE. **** ABSTRACT: En este documento se evalúan los pronósticos de la tasa de desempleo urbana en Colombia utilizando varias metodologías. La primera se basa en las propiedades estadísticas de la serie de tiempo de la tasa de desempleo. La segunda considera la relación entre el crecimiento del producto y los cambios en el desempleo, conocida como la Ley de Okun. Finalmente, con base en los microdatos de las encuestas de hogares se calculan los flujos de trabajadores del mercado laboral para pronosticar la tasa de desempleo de acuerdo con Barnichon y Nekarda (2013). La evaluación de los pronósticos fuera de muestra indica que el modelo de dos estados (ocupado-desocupado) es el mejor en horizontes superiores a cinco meses. Por su parte, los modelos ARIMA y la Ley de Okun compiten en precisión en horizontes de corto plazo. Cabe destacar que la combinación de los modelos de pronóstico genera ganancias significativas en todos los horizontes, alcanzando una reducción global de 23% en la raíz del error cuadrático medio. Classification-JEL: C53, E24, E27, E3, J64

Suggested Citation

  • Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1073
    DOI: 10.32468/be.1073
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1073
    Download Restriction: no

    File URL: https://libkey.io/10.32468/be.1073?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
    ---><---

    References listed on IDEAS

    as
    1. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    2. Baghestani, Hamid, 2008. "Federal Reserve versus private information: Who is the best unemployment rate predictor," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 101-110.
    3. Robert Shimer, 2012. "Reassessing the Ins and Outs of Unemployment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 127-148, April.
    4. Lasso-Valderrama, Francisco Javier, 2012. "La dinámica del desempleo urbano en Colombia," Chapters, in: Arango-Thomas, Luis Eduardo & Hamann-Salcedo, Franz Alonso (ed.), El mercado de trabajo en Colombia : hechos, tendencias e instituciones, chapter 3, pages 131-166, Banco de la Republica de Colombia.
    5. Gary Solon & Ryan Michaels & Michael W. L. Elsby, 2009. "The Ins and Outs of Cyclical Unemployment," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 84-110, January.
    6. Rothman Philip A, 2008. "Reconsideration of the Markov Chain Evidence on Unemployment Rate Asymmetry," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-18, September.
    7. Shigeru Fujita, 2011. "Dynamics of worker flows and vacancies: evidence from the sign restriction approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 89-121, January/F.
    8. Barnichon, Regis, 2012. "Vacancy posting, job separation and unemployment fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 315-330.
    9. Floros, Ch., 2005. "Forecasting the UK Unemployment Rate: Model Comparisons," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(4), pages 57-72.
    10. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    11. 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.
    12. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.
    13. Stephanie Aaronson & Bruce Fallick & Andrew Figura & Jonathan Pingle & William Wascher, 2006. "The Recent Decline in the Labor Force Participation Rate and Its Implications for Potential Labor Supply," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 37(1), pages 69-154.
    14. Skalin, Joakim & Teräsvirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(2), pages 202-241, April.
    15. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    16. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    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. Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 83-131.
    2. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
    3. Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
    4. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    6. Régis Barnichon, 2009. "Demand-driven job separation: reconciling search models with the ins and outs of unemployment," Finance and Economics Discussion Series 2009-24, Board of Governors of the Federal Reserve System (U.S.).
    7. Michele Campolieti & Deborah Gefang & Gary Koop, 2011. "Time Variation in the Dynamics of Worker Flows: Evidence from the US and Canada," Working Papers 1138, University of Strathclyde Business School, Department of Economics.
    8. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    9. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    10. Portugal, Pedro & Rua, António, 2018. "Zooming the Ins and Outs of the U.S. Unemployment with a Wavelet Lens," IZA Discussion Papers 11559, Institute of Labor Economics (IZA).
    11. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    12. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    13. Michael W. L. Elsby & Bart Hobijn & Aysegul Sahin, 2010. "The Labor Market in the Great Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(1 (Spring), pages 1-69.
    14. Shigeru Fujita & Makoto Nakajima, 2016. "Worker Flows and Job Flows: A Quantitative Investigation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 22, pages 1-20, October.
    15. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    16. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    17. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    18. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    19. Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.

    More about this item

    Keywords

    Forecasting; unemployment; VAR models; labour market flows; Pronósticos; desempleo; modelos VAR; flujos del mercado laboral;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:bdr:borrec:1073. 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: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.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.