IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v33y2017i2p359-372.html
   My bibliography  Save this article

Evaluating multi-step system forecasts with relatively few forecast-error observations

Author

Listed:
  • Hendry, David F.
  • Martinez, Andrew B.

Abstract

This paper develops a new approach for evaluating multi-step system forecasts with relatively few forecast-error observations. It extends the work of Clements and Hendry (1993) by using that of Abadir et al. (2014) to generate “design-free” estimates of the general matrix of the forecast-error second-moment when there are relatively few forecast-error observations. Simulations show that the usefulness of alternative methods deteriorates when their assumptions are violated. The new approach compares well with these methods and provides correct forecast rankings.

Suggested Citation

  • Hendry, David F. & Martinez, Andrew B., 2017. "Evaluating multi-step system forecasts with relatively few forecast-error observations," International Journal of Forecasting, Elsevier, vol. 33(2), pages 359-372.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:359-372
    DOI: 10.1016/j.ijforecast.2016.08.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207016300991
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2016.08.007?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
    2. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    3. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
    4. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    5. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "Reply to the comments on 'A vector error-correction forecasting model of the U.S. economy'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 613-614, December.
    6. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    7. William Veloce, 2004. "Forecasting Inbound Canadian Tourism: An Evaluation of Error Corrections Model Forecasts," Tourism Economics, , vol. 10(3), pages 263-280, September.
    8. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.
    9. Bårdsen, Gunnar & den Reijer, Ard & Jonasson, Patrik & Nymoen, Ragnar, 2012. "MOSES: Model for studying the economy of Sweden," Economic Modelling, Elsevier, vol. 29(6), pages 2566-2582.
    10. Pretis, Felix & Roser, Max, 2017. "Carbon dioxide emission-intensity in climate projections: Comparing the observational record to socio-economic scenarios," Energy, Elsevier, vol. 135(C), pages 718-725.
    11. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    12. Baillie, Richard T. & Morana, Claudio, 2009. "Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1577-1592, August.
    13. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
    14. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    15. Bowsher, Clive G. & Meeks, Roland, 2008. "The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1419-1437.
    16. Paap, Richard & Franses, Philip Hans & Hoek, Henk, 1997. "Mean shifts, unit roots and forecasting seasonal time series," International Journal of Forecasting, Elsevier, vol. 13(3), pages 357-368, September.
    17. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    18. Vahid, Farshid & Issler, Joao Victor, 2002. "The importance of common cyclical features in VAR analysis: a Monte-Carlo study," Journal of Econometrics, Elsevier, vol. 109(2), pages 341-363, August.
    19. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "A vector error-correction forecasting model of the US economy," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 569-598, December.
    20. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
    21. Zeng Tian & Swanson Norman R., 1998. "Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-21, January.
    22. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    23. Timothy L. McMurry & Dimitris N. Politis, 2010. "Banded and tapered estimates for autocovariance matrices and the linear process bootstrap," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 471-482, November.
    24. Abadir, Karim M. & Distaso, Walter & Žikeš, Filip, 2014. "Design-free estimation of variance matrices," Journal of Econometrics, Elsevier, vol. 181(2), pages 165-180.
    25. Schmidt, Peter, 1974. "The Asymptotic Distribution of Forecasts in the Dynamic Simulation of an Econometric Model," Econometrica, Econometric Society, vol. 42(2), pages 303-309, March.
    26. Martinez, Andrew B., 2015. "How good are US government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 31(2), pages 312-324.
    27. Dennis L. Hoffman & Robert H. Rasche, 1997. "STLS/US-VECM6.1: a vector error-correction forecasting model of the U. S. economy," Working Papers 1997-008, Federal Reserve Bank of St. Louis.
    28. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.
    29. De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.
    30. McMurry, Timothy L & Politis, D N, 2010. "Banded and Tapered Estimates for Autocovariance Matrices and the Linear Process Bootstrap," University of California at San Diego, Economics Working Paper Series qt5h9259mb, Department of Economics, UC San Diego.
    31. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.
    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. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    2. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    3. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    4. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    5. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    6. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    7. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    8. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    9. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    10. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    11. Håvard Hungnes, 2020. "Predicting the exchange rate path. The importance of using up-to-date observations in the forecasts," Discussion Papers 934, Statistics Norway, Research Department.
    12. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    13. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    14. Håvard Hungnes, 2018. "Encompassing tests for evaluating multi-step system forecasts invariant to linear transformations," Discussion Papers 871, Statistics Norway, Research Department.

    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. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    2. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    3. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    4. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    5. Guo, Zi-Yi, 2017. "Comparison of Error Correction Models and First-Difference Models in CCAR Deposits Modeling," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 17(4).
    6. Guillén, Osmani Teixeira & Hecq, Alain & Issler, João Victor & Saraiva, Diogo, 2015. "Forecasting multivariate time series under present-value model short- and long-run co-movement restrictions," International Journal of Forecasting, Elsevier, vol. 31(3), pages 862-875.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    8. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    9. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    10. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    11. Levent KORAP, 2008. "Exchange Rate Determination Of Tl/Us$:A Co-Integration Approach," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 7(1), pages 24-50, May.
    12. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    13. David F Hendry & John N J Muellbauer, 2018. "The future of macroeconomics: macro theory and models at the Bank of England," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 287-328.
    14. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    15. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    16. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damage," Econometrics, MDPI, vol. 8(2), pages 1-24, May.
    17. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    18. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    19. Heather M Anderson & Farshid Vahid, 2010. "VARs, Cointegration and Common Cycle Restrictions," Monash Econometrics and Business Statistics Working Papers 14/10, Monash University, Department of Econometrics and Business Statistics.
    20. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.

    More about this item

    Keywords

    Invariance; Forecast evaluation; Forecast error; Moment matrices; MSFE; GFESM;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:eee:intfor:v:33:y:2017:i:2:p:359-372. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.