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Automatic Modeling Methods for Univariate Series

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Author Info

  • Víctor Gómez
  • Agustín Maravall

Abstract

In this article, a unified approach to automatic modeling for univariate series is presented. First, ARIMA models and the classical methods for fitting these models to a given time series are reviewed. Second, some objective methods for model identification are considered and some algorithmical procedures for automatic model identification are described. Third, outliers are incorporated into the model and an algorithm, for automatic model identification in the presence of outliers is proposed.

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Bibliographic Info

Paper provided by Banco de Espa�a in its series Banco de Espa�a Working Papers with number 9808.

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Length: 50 pages
Date of creation: 1998
Date of revision:
Handle: RePEc:bde:wpaper:9808

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Related research

Keywords: MODELS ; TIME SERIES;

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Cited by:
  1. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
  2. Giorgio Fagiolo & Mauro Napoletano & Andrea Roventini, 2006. "Are Output Growth-Rate Distributions Fat-Tailed? Some Evidence from OECD Countries," Working Papers 36, University of Verona, Department of Economics.
  3. Jyh-Ying Peng & John A. D. Aston, . "The State Space Models Toolbox for MATLAB," Journal of Statistical Software, American Statistical Association, vol. 41(i06).
  4. Basher, Syed Abul & Fachin, Stefano, 2011. "The long-run relationship between savings and investment in oil-exporting developing countries: A case study of the Gulf Arab States," MPRA Paper 29077, University Library of Munich, Germany.
  5. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
  6. Meyler, Aidan, 1999. "The non-accelerating inflation rate of unemployment (NAIRU) in a small open economy: The irish context," MPRA Paper 11363, University Library of Munich, Germany.
  7. Trapero, Juan R. & Pedregal, Diego J., 2009. "Frequency domain methods applied to forecasting electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 727-735, September.
  8. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Banco de Espa�a Working Papers 0112, Banco de Espa�a.
  9. George Athanasopoulos & D.S. Poskitt & Farshid Vahid, 2007. "Two canonical VARMA forms: Scalar component models vis-à-vis the Echelon form," Monash Econometrics and Business Statistics Working Papers 10/07, Monash University, Department of Econometrics and Business Statistics, revised May 2009.
  10. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
  11. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 219, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
  12. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
  13. Agustín Maravall & Fernando J. Sánchez, 2000. "An Application of TRAMO-SEATS: Model Selection and Out-of-Sample Performance: the Swiss CPI Series," Banco de Espa�a Working Papers 0014, Banco de Espa�a.
  14. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).

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