Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

5 Issues per year


IMPACT FACTOR 2017: 0.855

CiteScore 2017: 0.76

SCImago Journal Rank (SJR) 2017: 0.668
Source Normalized Impact per Paper (SNIP) 2017: 0.894

Mathematical Citation Quotient (MCQ) 2017: 0.02

Online
ISSN
1558-3708
See all formats and pricing
More options …
Volume 19, Issue 3

Issues

Can we use seasonally adjusted variables in dynamic factor models?

Maximo Camacho
  • Corresponding author
  • Universidad de Murcia, Facultad de Economia y Empresa, Departamento de Metodos Cuantitativos para la Economia y la Empresa, 30100, Murcia, Spain
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Yuliya Lovcha / Gabriel Perez Quiros
Published Online: 2014-09-23 | DOI: https://doi.org/10.1515/snde-2013-0096

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.

This article offers supplementary material which is provided at the end of the article.

Keywords: dynamic factor models; seasonal adjustment; short-term forecasting.

JEL Classification: E32; C22; E27

References

  • Aruoba, B., and F. Diebold. 2010. “Real-time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions.” American Economic Review 100: 20–24.Web of ScienceCrossrefGoogle Scholar

  • Aruoba, B., F. Diebold, and C. Scotti. 2009. “Real-time Measurement of Business Conditions.” Journal of Business and Economic Statistics 7: 417–427.Web of ScienceCrossrefGoogle Scholar

  • Banbura, M., and M. Modugno. 2014. “Maximum Likelihood Estimation of Factor Models on Datasets with Arbitrary Pattern of Missing Data.” Journal of Applied Econometrics 29: 133–160.CrossrefGoogle Scholar

  • Boivin, J., and S. Ng. 2006. “Are more Data Always Better for Factor Analysis?” Journal of Econometrics 132: 169–194.Google Scholar

  • Bruce, A. G., and S.R. Jurke. 1996. “Non-Gaussian Seasonal Adjustment: X-12 ARIMA versus Robust Structural Models.” Journal of Forecasting 15: 305–327.Google Scholar

  • Camacho, M., and G. Perez-Quiros. 2010. “Introducing the Euro-STING: Short Term Indicator of Euro Area Growth.” Journal of Applied Econometrics 25: 663–694.Web of ScienceCrossrefGoogle Scholar

  • Camacho, M, G. Perez-Quiros, and P. Poncela. 2012. “Markov-switching Dynamic Factor Models in Real Time.” CEPR Discussion Paper no. 8866.Google Scholar

  • Clark, T., and K. West. 2007. “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138: 291–311.Web of ScienceGoogle Scholar

  • Geweke, J. F. 1977. “The Dynamic Factor Analysis of Economic Time Series Models.” In: Latent Variables in Socioeconomic Models, edited by D. Aigner and A. Goldberger. North-Holland, Amsterdam.Google Scholar

  • Geweke, J. F., and K. J. Singleton. 1981. “Maximum Likelihood “Confirmatory” Factor Analysis of Economic Time Series.” International Economic Review 22: 37–54.CrossrefGoogle Scholar

  • Hannan, E. J., R. D. Terrell, and N. E. Tuckwell. 1970. “The Seasonal Adjustment of Economic Time Series.” International Economic Review 11: 24–52.CrossrefGoogle Scholar

  • Harrison, P. J., and C. F. Stevens. 1976. “Bayesian Forecasting.” Journal of the Royal Statistical Society, Series B 38: 205–247.Google Scholar

  • Harvey, A. 1989. “Forecasting.” Structural Time Series Models and Kalman Filter. Cambridge: Cambridge University Press.Google Scholar

  • Poncela, P., and E. Ruiz. 2012. “More is Not Always Better: Back to the Kalman Filter in Dynamic Factor Models.” UC3M Working papers, Statistics and Econometrics, 12/17.Google Scholar

  • Stock, J., and M. Watson. 1991. “A Probability Model of the Coincident Economic Indicators.” In: Leading Economic Indicators, New Approaches and Forecasting Records, edited by Kajal Lahiri and Geoffrey Moore. Cambridge: Cambridge University Press.Google Scholar

About the article

Corresponding author: Maximo Camacho, Universidad de Murcia, Facultad de Economia y Empresa, Departamento de Metodos Cuantitativos para la Economia y la Empresa, 30100, Murcia, Spain, e-mail:


Published Online: 2014-09-23

Published in Print: 2015-06-01


Citation Information: Studies in Nonlinear Dynamics & Econometrics, Volume 19, Issue 3, Pages 377–391, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2013-0096.

Export Citation

©2015 by De Gruyter.Get Permission

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in