IDEAS home Printed from
   My bibliography  Save this paper

Automatic Modeling Methods for Univariate Series


  • Víctor Gómez
  • Agustín Maravall


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

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    1. Laurence Ball, 1994. "What Determines the Sacrifice Ratio?," NBER Chapters,in: Monetary Policy, pages 155-193 National Bureau of Economic Research, Inc.
    2. Gylfason, Thorvaldur & Herbertsson, Tryggvi Thor, 2001. "Does inflation matter for growth?," Japan and the World Economy, Elsevier, vol. 13(4), pages 405-428, December.
    3. Stanley Fischer & Franco Modigliani, 1978. "Towards an understanding of the real effects and costs of inflation," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 114(4), pages 810-833, December.
    4. Manuel Jaen & Agustin Molina, 1994. "Un análisis empírico de la tenencia y demanda de vivienda en Andalucía," Investigaciones Economicas, Fundación SEPI, vol. 18(1), pages 143-164, January.
    5. Timothy Kehoe & Antonio Manresa & Clemente Polo & Ferrán Sancho, 1989. "Un análisis de equilibrio general de la reforma fiscal de 1986 en España," Investigaciones Economicas, Fundación SEPI, vol. 13(3), pages 337-385, September.
    6. Martin S. Feldstein, 1999. "Capital Income Taxes and the Benefit of Price Stability," NBER Chapters,in: The Costs and Benefits of Price Stability, pages 9-46 National Bureau of Economic Research, Inc.
    7. Casey B. Mulligan & Xavier Sala-i-Martin, 1995. "Adoption of financial technologies: Implications for money demand and monetary policy," Economics Working Papers 134, Department of Economics and Business, Universitat Pompeu Fabra.
    8. José Manuel González-Páramo, 1991. "Imposición personal e incentivos fiscales al ahorro en España," Estudios Económicos, Banco de España;Estudios Económicos Homepage, number 46.
    9. George A. Akerlof & William R. Dickens & George L. Perry, 1996. "The Macroeconomics of Low Inflation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(1), pages 1-76.
    10. Ballard, Charles L & Shoven, John B & Whalley, John, 1985. "General Equilibrium Computations of the Marginal Welfare Costs of Taxes in the United States," American Economic Review, American Economic Association, vol. 75(1), pages 128-138, March.
    11. Martin S. Feldstein, 1997. "The Costs and Benefits of Going from Low Inflation to Price Stability," NBER Chapters,in: Reducing Inflation: Motivation and Strategy, pages 123-166 National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. 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.
    2. Meyler, Aidan, 1999. "The Non-Accelerating Inflation Rate of Unemployment (NAIRU) in a Small Open Economy: The Irish Context," Research Technical Papers 5/RT/99, Central Bank of Ireland.
    3. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    4. 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.
    5. 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.
    6. 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.
    7. Joan Paredes & Diego J. Pedregal & Javier J. Pérez, 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Working Papers 0935, Banco de España;Working Papers Homepage.
    8. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España;Working Papers Homepage.
    9. 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).
    10. 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.
    11. 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).
    12. 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.
    13. 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.).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.

    More about this item



    JEL classification:

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


    Access and download statistics


    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:bde:wpaper:9808. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (María Beiro. Electronic Dissemination of Information Unit. Research Department. Banco de España). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.