IDEAS home Printed from https://ideas.repec.org/p/ebg/iesewp/d-0446.html
   My bibliography  Save this paper

Evolución de la inflación en España

Author

Listed:
  • Ariño, Miguel A.

    (IESE Business School)

  • Canela, Miguel A.

    (Universidad de Barcelona)

Abstract

El objetivo de este documento es presentar diversos modelos que pueden ayudar a entender la evolución de la inflación en España, así como a hacer predicciones de la inflación a medio plazo. El estudio se basa en un análisis estadístico-econométrico de los datos de la inflación publicados por el Instituto Nacional de Estadística (INE). Entre los distintos modelos que se examinan, se escogen los que puedan resultar más útiles para entender la inflación. En este trabajo se distinguen los modelos univariantes, en los que sólo se usan los datos de la inflación, de los multivariantes, que usan, además, datos de otras variables macroeconómicas, como, por ejemplo, el producto interior bruto o la tasa de desempleo. Se dividen los modelos univariantes en dos grupos. Los del primer grupo serán modelos sencillos de regresión, y los del segundo, modelos de memoria larga. También se evalúa un modelo por su capacidad de predecir la inflación del año siguiente. Para ello, aplicando el modelo a la serie de inflación que acaba en diciembre de 1988, predecimos la tasa de inflación anual de 1989. Lo mismo hacemos para predecir la inflación de 1990, 1991, etc., hasta la del año 2000, usando siempre una serie que llega hasta el mes de diciembre del año anterior a aquel cuya inflación queremos predecir. Después, restamos, de la inflación predicha por el modelo, la inflación real, obteniendo el error de previsión. La calidad de un modelo la evaluamos mediante el error cuadrático medio, que es la media de los cuadrados de los errores de predicción obtenidos para los años 1989-2000. Algunos de los modelos que estudiamos usan datos mensuales, otros trimestrales, y en alguna ocasión, anuales. En cualquier caso, estamos interesados en la predicción de la tasa anual de inflación a un año vista, en el momento que se conoce la tasa de inflación mensual de diciembre del año anterior.

Suggested Citation

  • Ariño, Miguel A. & Canela, Miguel A., 2002. "Evolución de la inflación en España," IESE Research Papers D/446, IESE Business School.
  • Handle: RePEc:ebg:iesewp:d-0446
    as

    Download full text from publisher

    File URL: http://www.iese.edu/research/pdfs/DI-0446.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seamus Hogan & Marianne Johnson & Thérèse Laflèche, 2001. "Core Inflation," Technical Reports 89, Bank of Canada.
    2. Camba-Mendez, Gonzalo & Rodriguez-Palenzuela, Diego, 2003. "Assessment criteria for output gap estimates," Economic Modelling, Elsevier, vol. 20(3), pages 529-562, May.
    3. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    4. Luis J. Álvarez & María de los Llanos Matea, 1999. "Underlying Inflation Measures in Spain," Working Papers 9911, Banco de España.
    5. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 36, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dowd, Kevin & Cotter, John & Loh, Lixia, 2011. "U.S. Core Inflation: A Wavelet Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 15(4), pages 513-536, September.
    2. Gagik G. Aghajanyan, 2005. "Core inflation in a small transition country: choice of optimal measures," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 2(1), pages 83-110, June.
    3. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    4. Jonas Mockus, 2010. "On simulation of optimal strategies and Nash equilibrium in the financial market context," Journal of Global Optimization, Springer, vol. 48(1), pages 129-143, September.
    5. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    6. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    7. Luis Gil-Alana, 2004. "Forecasting the real output using fractionally integrated techniques," Applied Economics, Taylor & Francis Journals, vol. 36(14), pages 1583-1589.
    8. Nielsen, Morten Orregaard & Shimotsu, Katsumi, 2007. "Determining the cointegrating rank in nonstationary fractional systems by the exact local Whittle approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 574-596, December.
    9. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2022. "Globalization, long memory, and real interest rate convergence: a historical perspective," Empirical Economics, Springer, vol. 63(5), pages 2331-2355, November.
    10. Hassler, U. & Marmol, F. & Velasco, C., 2006. "Residual log-periodogram inference for long-run relationships," Journal of Econometrics, Elsevier, vol. 130(1), pages 165-207, January.
    11. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    12. Pierre Perron & Zhongjun Qu, 2007. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts," Boston University - Department of Economics - Working Papers Series wp2007-044, Boston University - Department of Economics.
    13. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    14. Geoffrey Ngene & Ann Nduati Mungai & Allen K. Lynch, 2018. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-38, June.
    15. Youwei Li & Xue-Zhong He, 2005. "Long Memory, Heterogeneity, and Trend Chasing," Computing in Economics and Finance 2005 113, Society for Computational Economics.
    16. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
    17. Karlis, Alexandros & Galanis, Girogos & Terovitis, Spyridon & Turner, Matthew, 2017. "Heterogeneity and Clustering of Defaults," Economic Research Papers 270011, University of Warwick - Department of Economics.
    18. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    19. Kosei Fukuda, 2010. "Three new empirical perspectives on the Hodrick–Prescott parameter," Empirical Economics, Springer, vol. 39(3), pages 713-731, December.
    20. Christos Christodoulou-Volos & Fotios Siokis, 2006. "Long range dependence in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1331-1338.

    More about this item

    Keywords

    inflacion;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:ebg:iesewp:d-0446. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Noelia Romero (email available below). General contact details of provider: https://edirc.repec.org/data/ienaves.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.