IDEAS home Printed from https://ideas.repec.org/p/chb/bcchwp/260.html
   My bibliography  Save this paper

Forecasting Chilean Industrial Production and Sales with Automated Procedures

Author

Listed:
  • Rómulo Chumacero

Abstract

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every forecasting horizon.

Suggested Citation

  • Rómulo Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Working Papers Central Bank of Chile 260, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:260
    as

    Download full text from publisher

    File URL: https://www.bcentral.cl/documents/33528/133326/DTBC_260.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Timmermann, Allan & Patton, Andrew, 2003. "Properties of Optimal Forecasts," CEPR Discussion Papers 4037, C.E.P.R. Discussion Papers.
    2. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Claudio Soto G., 2004. "Unemployment and Consumption in Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(1), pages 31-50, April.
    4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    7. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    8. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    9. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
    10. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    11. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    12. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    13. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    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. Roque Montero, 2012. "Does Linearity in the Dynamics of Inflation Gap and Unemployment Rate Matter?," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 27(1), pages 3-26, April.
    2. Norman Loayza & Pablo Fajnzylber & César Calderón, 2005. "Economic Growth in Latin America and the Caribbean : Stylized Facts, Explanations, and Forecasts," World Bank Publications - Books, The World Bank Group, number 7315, December.

    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. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    2. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    3. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    4. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    5. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    6. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
    7. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    8. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    9. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    10. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    11. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    12. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    13. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    14. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    15. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    16. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    17. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    18. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    19. Greg Tkacz & Carolyn A. Wilkins, 2006. "Linear and Threshold Forecasts of Output and Inflation with Stock and Housing Prices," Staff Working Papers 06-25, Bank of Canada.
    20. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.

    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:chb:bcchwp:260. 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: Alvaro Castillo (email available below). General contact details of provider: https://edirc.repec.org/data/bccgvcl.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.