IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v7y2003i2n4.html
   My bibliography  Save this article

A Nonlinear Approach to Forecasting with Leading Economic Indicators

Author

Listed:
  • Jagric Timotej

Abstract

The classical NBER leading indicators model was built solely within a linear framework. With recent developments in nonlinear time-series analysis, several authors have begun to examine the forecasting properties of nonlinear models in the field of forecasting business cycles. The research presented in this paper focuses on the development of a new approach to forecasting with leading indicators based on neural networks. Empirical results are presented for forecasting the Index of Industrial Production. The results demonstrate that a superior performance can be obtained relative to the classical model.

Suggested Citation

  • Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
    • Handle: RePEc:bpj:sndecm:v:7:y:2003:i:2:n:4
    DOI: 10.2202/1558-3708.1135
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1558-3708.1135
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1558-3708.1135?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stekler, H. O., 1991. "Macroeconomic forecast evaluation techniques," International Journal of Forecasting, Elsevier, vol. 7(3), pages 375-384, November.
    2. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    3. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    4. Regina Kaiser & Agustín Maravall, 1999. "Estimation of the business cycle: A modified Hodrick-Prescott filter," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 175-206.
    5. Sebastjan Strasek & Timotej Jagric, 2002. "Cyclical patterns in aggregate economic activity of Slovene economy," Applied Economics, Taylor & Francis Journals, vol. 34(14), pages 1813-1819.
    6. Jaditz, Ted & Riddick, Leigh A. & Sayers, Chera L., 1998. "MULTIVARIATE NONLINEAR FORECASTING Using Financial Information to Forecast the Real Sector," Macroeconomic Dynamics, Cambridge University Press, vol. 2(3), pages 369-382, September.
    7. Randal Verbrugge Randal Verbrugge, 1997. "Investigating Cyclical Asymmetries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-10, April.
    8. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    9. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, January.
    10. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    11. Wojciech W. Charemza & Derek F. Deadman, 1992. "New Directions In Econometric Practice," Books, Edward Elgar Publishing, number 84.
    12. Saul H. Hymans, 1973. "On the Use of Leading Indicators to Predict Cyclical Turning Points," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 4(2), pages 339-384.
    13. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
    14. Kim Jeong-Ryeol & Mittnik Stefan & Rachev Svetlozar T., 1996. "Detecting Asymmetries in Observed Linear Time Series and Unobserved Disturbances," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(3), pages 1-15, October.
    15. Timotej Jagri, 2002. "Measuring Business Cycles: The Case of Slovenia," Eastern European Economics, Taylor & Francis Journals, vol. 40(1), pages 63-87, January.
    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. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    2. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    3. Chian, Abraham C.-L. & Rempel, Erico L. & Rogers, Colin, 2006. "Complex economic dynamics: Chaotic saddle, crisis and intermittency," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1194-1218.

    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. Timotej Jagric, 2003. "Forecasting with leading economic indicators - a non-linear approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(1), pages 68-83.
    2. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    3. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 95-156, National Bureau of Economic Research, Inc.
    4. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    5. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    6. James H. Stock & Mark W. Watson, 1990. "Business Cycle Properties of Selected U.S. Economic Time Series, 1959-1988," Working Papers 1990-1, Princeton University. Economics Department..
    7. James H. Stock & Mark W. Watson, 1990. "Business Cycle Properties of Selected U.S. Economic Time Series, 1959-1988," NBER Working Papers 3376, National Bureau of Economic Research, Inc.
    8. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Valentina Aprigliano & Danilo Liberati, 2021. "Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time," Manchester School, University of Manchester, vol. 89(S1), pages 76-96, September.
    10. 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.
    11. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    12. 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.
    13. Sung Il Hong & Michael Mondello & Dennis Coates, 2011. "An Examination of the Effects of the Recent Economic Crisis on Major League Baseball (MLB) Attendance Demand," Working Papers 1123, International Association of Sports Economists;North American Association of Sports Economists.
    14. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    15. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    16. Oleg Korenok & Stanislav Radchenko, 2004. "Monetary Policy Effect on the Business Cycle Fluctuations: Output vs. Index Measures of the Cycle," Macroeconomics 0409015, University Library of Munich, Germany, revised 20 Sep 2004.
    17. Fabio H. Nieto & Luis Fernando Melo, 2001. "About a Coincident Index for the State of the Economy," Borradores de Economia 1938, Banco de la Republica.
    18. Ahking, Francis W., 2014. "Measuring U.S. business cycles: A comparison of two methods and two indicators of economic activities," Journal of Economic and Social Measurement, IOS Press, issue 4, pages 199-216.
    19. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    20. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.

    More about this item

    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:bpj:sndecm:v:7:y:2003:i:2:n:4. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.