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Can we use seasonally adjusted variables in dynamic factor models?

Author

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  • Camacho Maximo

    (Universidad de Murcia, Facultad de Economia y Empresa, Departamento de Metodos Cuantitativos para la Economia y la Empresa, 30100, Murcia, Spain)

  • Lovcha Yuliya

    (Universitat Rovira i Virgili, Departmento de Economia, Av. Universitat, 1, 43204 Reus, Spain)

  • Quiros Gabriel Perez

    (Banco de España and CEPR. Calle Alcalá 48, 28014 Madrid, Spain)

Abstract

We examine the short-term performance of two alternative approaches of forecasting from dynamic factor models. The first approach extracts the seasonal component of the individual variables before estimating the model, while the alternative uses the non seasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show the that the common practice of using seasonally adjusted data in this type of models is very accurate in terms of forecasting ability. Using five coincident indicators, we illustrate this result for US data.

Suggested Citation

  • Camacho Maximo & Lovcha Yuliya & Quiros Gabriel Perez, 2015. "Can we use seasonally adjusted variables in dynamic factor models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 377-391, June.
    • Handle: RePEc:bpj:sndecm:v:19:y:2015:i:3:p:377-391:n:3
    DOI: 10.1515/snde-2013-0096
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    References listed on IDEAS

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    1. 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.
    2. 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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    5. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    6. Poncela, Pilar & Ruiz Ortega, Esther, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
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