IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v23y2004i4p275-296.html
   My bibliography  Save this article

Updating ARMA predictions for temporal aggregates

Author

Listed:
  • Yue Fang
  • Sergio G. Koreisha

    (University of Oregon, USA)

Abstract

This article develops and extends previous investigations on the temporal aggregation of ARMA predications. Given a basic ARMA model for disaggregated data, two sets of predictors may be constructed for future temporal aggregates: predictions based on models utilizing aggregated data or on models constructed from disaggregated data for which forecasts are updated as soon as the new information becomes available. We show that considerable gains in efficiency based on mean-square-error-type criteria can be obtained for short-term predications when using models based on updated disaggregated data. However, as the prediction horizon increases, the gain in using updated disaggregated data diminishes substantially. In addition to theoretical results associated with forecast efficiency of ARMA models, we also illustrate our findings with two well-known time series. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • Yue Fang & Sergio G. Koreisha, 2004. "Updating ARMA predictions for temporal aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 275-296.
    • Handle: RePEc:jof:jforec:v:23:y:2004:i:4:p:275-296
    DOI: 10.1002/for.913
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.913
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.913?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
    ---><---

    References listed on IDEAS

    as
    1. Brown, Lawrence D., 1993. "Earnings forecasting research: its implications for capital markets research," International Journal of Forecasting, Elsevier, vol. 9(3), pages 295-320, November.
    2. Brown, Lawrence D., 1993. "Reply to commentaries on "Earnings forecasting research: its implications for capital markets research"," International Journal of Forecasting, Elsevier, vol. 9(3), pages 343-344, November.
    3. Merville, Larry J. & Pieptea, Dan R., 1989. "Stock-price volatility, mean-reverting diffusion, and noise," Journal of Financial Economics, Elsevier, vol. 24(1), pages 193-214, September.
    4. Nijman, Theo E & Palm, Franz C, 1990. "Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 405-415, October.
    5. Koreisha, Sergio G & Pukkila, Tarmo, 1995. "A Comparison between Different Order-Determination Criteria for Identification of ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 127-131, January.
    6. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
    7. Hasbrouck, Joel, 1993. "Assessing the Quality of a Security Market: A New Approach to Transaction-Cost Measurement," The Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 191-212.
    8. Daniel O. Stram & William W. S. Wei, 1986. "Temporal Aggregation In The Arima Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(4), pages 279-292, July.
    9. William W. S. Wei, 1978. "Some Consequences of Temporal Aggregation in Seasonal Time Series Models," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 433-448, National Bureau of Economic Research, Inc.
    10. Ali, Ashiq & Zarowin, Paul, 1992. "Permanent versus transitory components of annual earnings and estimation error in earnings response coefficients," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 249-264, August.
    11. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
    12. Brown, Philip, 1993. "Comments on 'Earnings forecasting research: its implications for capital markets research' by L. Brown," International Journal of Forecasting, Elsevier, vol. 9(3), pages 331-335, November.
    13. Sergio G. Koreisha & Yue Fang, 2001. "Generalized least squares with misspecified serial correlation structures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 515-531.
    14. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
    15. Den Butter, F. A. G., 1976. "The use of monthly and quarterly data in an ARMA model," Journal of Econometrics, Elsevier, vol. 4(4), pages 311-324, November.
    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. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
    3. Pena-Levano, Luis M. & Ramirez, Octavio & Renteria-Pinon, Mario, 2015. "Efficiency Gains in Commodity Forecasting with High Volatility in Prices using Different Levels of Data Aggregation," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205740, Agricultural and Applied Economics Association.
    4. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    5. Pena-Levano, Luis M & Foster, Kenneth, 2016. "Efficiency gains in commodity forecasting using disaggregated levels versus more aggregated predictions," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235792, Agricultural and Applied Economics Association.
    6. Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
    7. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.
    8. Alexandre Petkovic, 2009. "Three essays on exotic option pricing, multivariate Lévy processes and linear aggregation of panel models," ULB Institutional Repository 2013/210357, ULB -- Universite Libre de Bruxelles.

    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. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    2. Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
    3. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Mest, David P. & Plummer, Elizabeth, 1999. "Transitory and persistent earnings components as reflected in analysts' short-term and long-term earnings forecasts: evidence from a nonlinear model," International Journal of Forecasting, Elsevier, vol. 15(3), pages 291-308, July.
    5. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    6. Jumming Hsu & Xu-Ming Wang & Chunchi Wu, 1998. "The Role of Earnings Information in Corporate Dividend Decisions," Management Science, INFORMS, vol. 44(12-Part-2), pages 173-191, December.
    7. S. P. Kothari & Charles Wasley, 2019. "Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years," Journal of Accounting Research, Wiley Blackwell, vol. 57(5), pages 1117-1159, December.
    8. Mohamed Naceur Mahjoubi & Ezzeddine Abaoub, 2015. "Earnings Response Coefficient as a Measure of Market Expectations: Evidence from Tunis Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 377-389.
    9. He, Shuoyuan & Narayanamoorthy, Ganapathi (Gans), 2020. "Earnings acceleration and stock returns," Journal of Accounting and Economics, Elsevier, vol. 69(1).
    10. Barniv, Ran, 2009. "Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 18(2), pages 101-118.
    11. Lin, Hai & Tao, Xinyuan & Wu, Chunchi, 2022. "Forecasting earnings with combination of analyst forecasts," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 133-159.
    12. Renê Coppe Pimentel & Andson Braga de Aguiar, 2012. "Persistence of quarterly earnings: an empirical investigation in Brazil," Brazilian Business Review, Fucape Business School, vol. 9(Special I), pages 38-54, March.
    13. Martin, Anna D. & Mauer, Laurence J., 2003. "Exchange rate exposures of US banks: A cash flow-based methodology," Journal of Banking & Finance, Elsevier, vol. 27(5), pages 851-865, May.
    14. Bert De Bruijn & Philip Hans Franses, 2018. "How Informative Are Earnings Forecasts? †," JRFM, MDPI, vol. 11(3), pages 1-20, July.
    15. Bert de Bruijn & Philip Hans Franses, 2015. "How Informative are the Unpredictable Components of Earnings Forecasts?," Tinbergen Institute Discussion Papers 15-032/III, Tinbergen Institute.
    16. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    17. Higgins, Huong, 2013. "Can securities analysts forecast intangible firms’ earnings?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 155-174.
    18. Kjell Bjørn Nordal & Randi Næs, 2012. "Mean Reversion in Profitability for Non†listed Firms," European Financial Management, European Financial Management Association, vol. 18(5), pages 929-949, November.
    19. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    20. van Lent, L.A.G.M., 1999. "Incomplete contracting theory in empirical accounting research," Other publications TiSEM 088f797d-9fa4-4081-98f4-1, Tilburg University, School of Economics and Management.

    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:jof:jforec:v:23:y:2004:i:4:p:275-296. 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: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.