IDEAS home Printed from https://ideas.repec.org/a/dug/actaec/y2013i1p126-136.html
   My bibliography  Save this article

Filters or Holt Winters Technique to Improve the Forecasts for USA Inflation Rate?

Author

Listed:
  • Mihaela Bratu (Simionescu)

    (Academy of Economic Studies, Bucharest,Romania)

Abstract

In this study, transformations of SPF inflation forecasts were made in order to get more accurate predictions. The filters application and Holt Winters technique were chosen as possible strategies of improving the predictions accuracy. The quarterly inflation rate forecasts (1975 Q1-2012 Q3) of USA made by SPF were transformed using an exponential smoothing technique- Holt Winters- and these new predictions are better than the initial ones for all forecasting horizons of 4 quarters. Some filters were applied to SPF forecasts (Hodrick- Prescott, Band-Pass and Christiano-Fitzegerald filters), but Holt Winters method was superior. Full sample asymmetric (Christiano-Fitzegerald) and Band-Pass filter smoothed values are more accurate than the SPF expectations only for some forecast horizons.

Suggested Citation

  • Mihaela Bratu (Simionescu), 2013. "Filters or Holt Winters Technique to Improve the Forecasts for USA Inflation Rate?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(1), pages 126-136, February.
    • Handle: RePEc:dug:actaec:y:2013:i:1:p:126-136
    as

    Download full text from publisher

    File URL: http://journals.univ-danubius.ro/index.php/oeconomica/article/view/1572
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    2. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    3. Razzak, W., 1997. "The Hodrick-Prescott technique: A smoother versus a filter: An application to New Zealand GDP," Economics Letters, Elsevier, vol. 57(2), pages 163-168, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Mihaela Simionescu (Bratu), 2014. "The Performance of Predictions Based on the Dobrescu Macromodel for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 179-195, October.
    2. Simionescu, Mihaela, 2014. "New Strategies to Improve the Accuracy of Predictions based on Monte Carlo and Bootstrap Simulations: An Application to Bulgarian and Romanian Inflation || Nuevas estrategias para mejorar la exactitud," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 18(1), pages 112-129, December.
    3. Mihaela Simionescu, 2015. "A New Technique based on Simulations for Improving the Inflation Rate Forecasts in Romania," Working Papers of Institute for Economic Forecasting 150206, Institute for Economic Forecasting.
    4. Mihaela Bratu, 2013. "New Methods of Evaluating the Forecasts Accuracy: A Case Study for USA Inflation," Business and Economic Research, Macrothink Institute, vol. 3(1), pages 21-37, June.
    5. Mihaela Simionescu, 2015. "The Improvement of Unemployment Rate Predictions Accuracy," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(3), pages 274-286.

    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. Botshekan, Mahmoud & Lucas, André, 2017. "Long-Term versus Short-Term Contingencies in Asset Allocation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2277-2303, October.
    2. Lubos Hanus & Lukas Vacha, 2015. "Business cycle synchronization of the Visegrad Four and the European Union," Working Papers IES 2015/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2015.
    3. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    4. Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario & Altissimo, Filippo & Cristadoro, Riccardo & Veronese, Giovanni & Bassanetti, Antonio, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    5. Marco Gallegati, 2019. "A system for dating long wave phases in economic development," Journal of Evolutionary Economics, Springer, vol. 29(3), pages 803-822, July.
    6. Jaromir Benes & David Vavra, 2004. "Eigenvalue Decomposition of Time Series with Application to the Czech Business Cycle," Working Papers 2004/08, Czech National Bank.
    7. Rabanal, Pau & Rubio-Ramírez, Juan F., 2015. "Can international macroeconomic models explain low-frequency movements of real exchange rates?," Journal of International Economics, Elsevier, vol. 96(1), pages 199-211.
    8. Azcona, Nestor, 2022. "Trade and business cycle synchronization: The role of common trade partners," International Economics, Elsevier, vol. 170(C), pages 190-201.
    9. Ageliki Anagnostou & Ioannis Panteladis & Maria Tsiapa, 2015. "Disentangling different patterns of business cycle synchronicity in the EU regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 615-641, August.
    10. Cuddington, John T. & Nülle, Grant, 2014. "Variable long-term trends in mineral prices: The ongoing tug-of-war between exploration, depletion, and technological change," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 224-252.
    11. Siem Jan Koopman & Joao Valle e Azevedo, 2003. "Measuring Synchronisation and Convergence of Business Cycles," Tinbergen Institute Discussion Papers 03-052/4, Tinbergen Institute.
    12. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    13. Lubik, Thomas A. & Matthes, Christian & Verona, Fabio, 2019. "Assessing U.S. aggregate fluctuations across time and frequencies," Bank of Finland Research Discussion Papers 5/2019, Bank of Finland.
    14. Gallegati, Marco & Giri, Federico & Palestrini, Antonio, 2019. "DSGE model with financial frictions over subsets of business cycle frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 152-163.
    15. Luca Benati, 2001. "Band-pass filtering, cointegration, and business cycle analysis," Bank of England working papers 142, Bank of England.
    16. Michael Funke, 2005. "Inflation in Mainland China - Modelling a Roller Coaster Ride," Quantitative Macroeconomics Working Papers 20507, Hamburg University, Department of Economics.
    17. Martínez, Juan Francisco & Oda, Daniel, 2021. "Characterization of the Chilean financial cycle, early warning indicators and implications for macro-prudential policies," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(1).
    18. Johan Fourie & Willem H. Boshoff, 2008. "Ship traffic and the economy of the Cape Colony: 1652-1793," Working Papers 089, Economic Research Southern Africa.
    19. Richard Ashley & Randal Verbrugge, 2009. "Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 4-20.
    20. Henk C. Kranendonk & Jan Bonenkamp & Johan P. Verbruggen, 2004. "A Leading Indicator for the Dutch Economy – Methodological and Empirical Revision of the CPB System," CESifo Working Paper Series 1200, CESifo.

    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:dug:actaec:y:2013:i:1:p:126-136. 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: Daniela Robu (email available below). General contact details of provider: https://edirc.repec.org/data/fedanro.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.