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Automatic Modeling Methods for Univariate Series

Author

Listed:
  • 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.

Suggested Citation

  • Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Working Papers 9808, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:9808
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    Cited by:

    1. 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.
    2. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    3. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, De Gruyter Open, vol. 31(4), pages 627-647, December.
    4. 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.
    5. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    6. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Working Paper Series 1132, European Central Bank.
    7. 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.
    8. Syed Abul Basher & Stefano Fachin, 2013. "The long-run relationship between savings and investment in oil-exporting developing countries: a case study of the Gulf Arab states," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 37(4), pages 429-446, December.
    9. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España;Working Papers Homepage.
    10. 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).
    11. Kaiser, Regina & Maravall, Agustín, 1999. "Seasonal outliers in time series," DES - Working Papers. Statistics and Econometrics. WS 6333, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. 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.
    14. 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.
    15. Peng, Jyh-Ying & Aston, John A. D., 2011. "The State Space Models Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i06).
    16. 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.).
    17. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    18. Agustín Maravall & Fernando J. Sánchez, 2000. "An Application of TRAMO-SEATS: Model Selection and Out-of-Sample Performance: the Swiss CPI Series," Working Papers 0014, Banco de España;Working Papers Homepage.

    More about this item

    Keywords

    MODELS ; TIME SERIES;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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