Skip to main content Accessibility help
×
Hostname: page-component-7479d7b7d-rvbq7 Total loading time: 0 Render date: 2024-07-10T17:01:45.609Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 October 2014

Philip Hans Franses
Affiliation:
Erasmus Universiteit Rotterdam
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Expert Adjustments of Model Forecasts
Theory, Practice and Strategies for Improvement
, pp. 119 - 125
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Armstrong, J. S. (2001), Combining forecasts, in Armstrong, J. S. (ed.), The Principles of Forecasting: A Handbook for Researchers and Practitioners, Norwell, MA: Kluwer.CrossRefGoogle Scholar
Armstrong, J. S. and Pagell, R. (2003), The ombudsman: Reaping benefits from management research: Lessons from the forecasting principles project, Interfaces, 33, 91–7.CrossRefGoogle Scholar
Athanasopoulos, G. and Hyndman, R. J. (2011), The value of feedback in forecasting competitions, International Journal of Forecasting, 27, 845–9.CrossRefGoogle Scholar
Auster, P. (2012) Winter Journal, London: Picador.Google Scholar
Balzer, W. K., Sulsky, L. M., Hammer, L. B. and Sumner, K. E. (1992), Task information, cognitive information, or functional validity information: Which components of cognitive feedback affect performance?, Organizational Behavior and Human Decision Processes, 53, 35–54.CrossRefGoogle Scholar
Barber, B. and Odean, T. (2001), Boys will be boys: Gender, overconfidence, and common stock investment, Quarterly Journal of Economics, 116, 261–92.CrossRefGoogle Scholar
Belsley, D. A. (1988), Modeling and forecasting reliability, International Journal of Forecasting, 4, 427–47.CrossRefGoogle Scholar
Beyer, S. and Bowden, E. (1997), Gender differences in self-perceptions: Convergent evidence from three measures of accuracy and bias, Personality and Social Psychology Bulletin, 23, 157–72.CrossRefGoogle Scholar
Blattberg, R. C. and Hoch, S. J. (1990), Database models and managerial intuition: 50% model + 50% manager, Management Science, 36, 887–99.CrossRefGoogle Scholar
Björkman, M. (1972), Feedforward and feedback as determiners of knowledge and policy-notes on a neglected issue, Scandinavian Journal of Psychology, 13, 152–8.CrossRefGoogle Scholar
Bolger, F. and Önkal-Atay, D. (2004), The effects of feedback on judgmental interval predictions, International Journal of Forecasting, 20, 29–39.CrossRefGoogle Scholar
Boulaksil, Y. and Franses, P. H. (2009), Experts’ stated behavior, Interfaces, 39, 168–71.CrossRefGoogle Scholar
Bunn, D. W. (1992), Synthesis of expert judgment and statistical forecasting models or decision support, in Wright, G. and Bolger, F. (eds.), Expertise and Decision Support, New York: Plenum, 251–68.CrossRefGoogle Scholar
Bunn, D. W. and Salo, A. A. (1996), Adjustment of forecasts with model consistent expectations, International Journal of Forecasting, 12, 163–70.CrossRefGoogle Scholar
Camerer, Colin F. (1989), Does the basketball market believe in the ‘hot hand’?, American Economic Review, 74, 1257–61.Google Scholar
Chen, C. and Liu, L.-M. (1993), Joint estimation of model parameters and outlier effects in time series, Journal of the American Statistical Association, 88, 284–97.Google Scholar
Chevillon, G. (2007), Direct multi-step estimation and forecasting, Journal of Economic Surveys, 21, 746–85.CrossRefGoogle Scholar
Chang, C.-L., Franses, P. H. and McAleer, M. (2011), How accurate are government forecasts of economic fundamentals? The case of Taiwan, International Journal of Forecasting, 27, 1066–75.CrossRefGoogle Scholar
Chang, C.-L., de Bruijn, B., Franses, P. H. and McAleer, M. (2013), Analyzing fixed-event forecast revisions, International Journal of Forecasting, 29, 622–7.CrossRefGoogle Scholar
Christoffersen, P. and Diebold, F. X. (1996), Further results on forecasting and model selection under asymmetric loss, Journal of Applied Econometrics, 11, 561–71.3.0.CO;2-S>CrossRefGoogle Scholar
Christoffersen, P. and Diebold, F. X. (1997), Optimal prediction under asymmetric loss, Econometric Theory, 13, 808–17.CrossRefGoogle Scholar
Clark, T. E. and McCracken, M. W. (2001), Tests of equal forecast accuracy and encompassing for nested models, Journal of Econometrics, 105, 85–110.CrossRefGoogle Scholar
Clark, T. E. and McCracken, M. W. (2005), Evaluating direct multi-step forecasts, Econometric Reviews, 24, 369–404.CrossRefGoogle Scholar
CPB (1992), FKSEC, A Macro-econometric Model for the Netherlands, Leiden: Stenfert Kroese.Google Scholar
CPB (2003), SAFE, A quarterly model of the Dutch economy for short-term analyses, CPB Document 42.
Croson, R. and Gneezy, U. (2009), Gender differences in preferences, Journal of Economic Literature, 47, 448–74.CrossRefGoogle Scholar
Croson, R. and Gneezy, U. (1979), The robust beauty of improper linear models in decision making, American Psychologist, 34, 571–82.Google Scholar
De Bondt, W. F. M. and Thaler, R. H. (1987), Further evidence on investor overreaction and stock market seasonality, Journal of Finance, 42, 557–81.CrossRefGoogle Scholar
De Bruijn, B. and Franses, P. H. (2012), Managing sales forecasters, Tinbergen Institute Discussion Paper 12–131/III, Erasmus University Rotterdam.
Diamantopoulos, A. and Mathews, B. P. (1989), Factors affecting the nature and effectiveness of subjective revision in sales forecasting: An empirical study, Managerial and Decision Economics, 10, 51–9.CrossRefGoogle Scholar
Diebold, F. X. and Mariano, R. S. (1995), Comparing predictive accuracy, Journal of Business and Economic Statistics, 13, 253–63.Google Scholar
Don, H. (2004), How econometric models help policy makers: theory and practice, CPB Discussion Paper 27, The Hague.
Don, H. and Verbruggen, J. P. (2006), Models and methods for economic policy: 60 years of evolution at CPB, Statistica Neerlandica, 60, 145–70.CrossRefGoogle Scholar
Donihue, M. R. (1993), Evaluating the role judgment plays in forecast accuracy, Journal of Forecasting, 12, 81–92.CrossRefGoogle Scholar
Durham, G. R., Hertzel, M. G. and Martin, J. S. (2005), The market impact of trends and sequences in performance: New evidence, Journal of Finance, 60, 2551–69.CrossRefGoogle Scholar
Eckel, C. C. and Grossman, P. J. (2008), Forecasting risk attitudes: An experimental study using actual and forecast gamble attitudes, Journal of Economic Behavior and Organization, 68, 1–17.CrossRefGoogle Scholar
Edmundson, R., Lawrence, M. and O’Connor, M. (1988), The use of non time series information in time series forecasting, Journal of Forecasting, 7, 201–12.CrossRefGoogle Scholar
Elliott, G., Komunjer, I. and Timmermann, A. (2005), Estimation and testing of forecast rationality under flexible loss, Review of Economic Studies, 72, 1107–25.CrossRefGoogle Scholar
Eriksson, K. and Simpson, B. (2010), Emotional reactions to losing explain gender differences in entering a risky lottery, Judgment and Decision Making, 3, 159–63.Google Scholar
Ferguson, T. (1967), Mathematical Statistics: A Decision Theoretic Approach, New York: Academic Press.Google Scholar
Fildes, R., and Goodwin, P. (2007), Good and bad judgment in forecasting: Lessons from four companies, Foresight: The International Journal of Applied Forecasting, 8, 5–10.Google Scholar
Fildes, R., Goodwin, P., Lawrence, M. and Nikolopoulos, K. (2009), Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning, International Journal of Forecasting, 25, 3–23.CrossRefGoogle Scholar
Fischoff, B. (1988), Judgemental aspects of forecasting: Needs and possible trends, International Journal of Forecasting, 4, 331–9.CrossRefGoogle Scholar
Fox, A. J. (1972), Outliers in time series, Journal of the Royal Statistical Society B, 34, 350–63.Google Scholar
Franses, P. H. (1998), Time Series Models for Business and Economic Forecasting, Cambridge University Press.Google Scholar
Franses, P. H. (2004), Do we think we make better forecasts than in the past?: A survey of academics, Interfaces, 34, 466–8.CrossRefGoogle Scholar
Franses, P. H. (2011a), Marketing and sales, in Clements, M. P. and Hendry, D. F. (eds.), The Oxford Handbook on Economic Forecasting, Oxford: Oxford University Press, ch. 25.Google Scholar
Franses, P. H. (2011b), Averaging model forecasts and expert forecasts: Why does it work?, Interfaces, 41, 177–81.CrossRefGoogle Scholar
Franses, P. H. (2013a) Improving judgmental adjustment of model-based Forecasts, Mathematics and Computers in Simulation, 93, 1–8.CrossRefGoogle Scholar
Franses, P. H. (2013b), Evaluating CPBs forecasts, Manuscript under revision for re-submission.
Franses, P. H., Kranendonk, H. and Lanser, D. (2011), One model and various experts: Evaluating Dutch macroeconomics forecasts, International Journal of Forecasting, 27, 482–95.CrossRefGoogle Scholar
Franses, P. H. and Legerstee, R. (2009), Properties of expert adjustments on model-based SKU-level forecasts, International Journal of Forecasting, 25, 35–47.CrossRefGoogle Scholar
Franses, P. H. and Legerstee, R. (2010), Do experts’ adjustments on model-based SKU-level forecasts improve forecast quality?Journal of Forecasting, 29, 331–40.Google Scholar
Franses, P. H. and Legerstee, R. (2011a), Experts’ adjustment to model-based SKU-level forecasts: Does the forecast horizon matter?, Journal of the Operational Research Society, 62, 537–43.CrossRefGoogle Scholar
Franses, P. H. and Legerstee, R. (2011b), Combining SKU-level sales forecasts from models and experts, Experts Systems with Applications, 38, 2365–70.CrossRefGoogle Scholar
Franses, P. H. and Legerstee, R. (2013), Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?, International Journal of Forecasting, 29, 80–7.CrossRefGoogle Scholar
Franses, P. H., Legerstee, R. and Paap, R. (2011), Estimating loss functions of experts, Tinbergen Institute Discussion Paper 177/4, Erasmus School of Economics.
Franses, P. H., McAleer, M. and Legerstee, R. (2009), Expert opinion versus expertise in forecasting, Statistica Neerlandica, 63, 334–46.CrossRefGoogle Scholar
Franses, P. H., McAleer, M. and Legerstee, R. (2014), Evaluating macroeconomic forecasts: A concise review of some recent developments, Journal of Economic Surveys, 28, 195–208.CrossRefGoogle Scholar
Franses, P. H. and Paap, R. (2002), Censored latent effects autoregression, with an application to US unemployment, Journal of Applied Econometrics, 17, 347–66.CrossRefGoogle Scholar
Glisky, E. L. (2007), Changes in cognitive function in human aging, in Riddle, D. R. (ed.), Brain Aging: Models, Methods, Mechanisms, Boca Raton, FL: CRC Press, pp. 1–15.Google ScholarPubMed
Gönül, S., Önkal, D. and Goodwin, P. (2009), Expectations, use and judgmental adjustment of external financial and economic forecasts: An empirical investigation, International Journal of Forecasting, 28, 19–37.CrossRefGoogle Scholar
Gönül, M. S., Önkal, D. and Lawrence, M. (2006), The effects of structural characteristics of explanations on use of a DSS, Decision Support Systems, 42, 1481–93.CrossRefGoogle Scholar
Goodwin, P. (2000), Improving the voluntary integration of statistical forecasts and judgment, International Journal of Forecasting, 16, 85–99.CrossRefGoogle Scholar
Goodwin, P. and Fildes, R. (1999), Judgmental forecasts of time series affected by special events: Does providing a statistical forecast improve accuracy?, Journal of Behavioral Decision Making, 12, 37–53.3.0.CO;2-8>CrossRefGoogle Scholar
Granger, C. W. J. and Morris, M. J. (1976), Time series modelling and interpretation, Journal of the Royal Statistical Society A, 139, 246–57.CrossRefGoogle Scholar
Granger, C. W. J. and Newbold, P. (1986), Forecasting Economic Time Series, San Diego: Academic Press.Google Scholar
Gysler, M., Kruse, J. B. and Schubert, R. (2002), Ambiguity and gender differences in financial decision making: An experimental examination of competence and confidence effects, Unpublished working paper, Swiss Federal Institute of Technology.
Haitovsky, Y. And Treyz, G. I. (1972), Forecasts with quarterly macroeconomic models, equation adjustments and benchmark predictions: the U.S. experience, Review of Economics and Statistics, 54, 317–25.CrossRefGoogle Scholar
Harvey, N (1995), Why are judgments less consistent in less predictable task situations?Organizational Behavior and Human Decision Processes, 63, 247–63.CrossRefGoogle Scholar
Heath, C. and Tversky, A. (1991), Preference and belief: Ambiguity and competence in choice under uncertainty, Journal of Risk and Uncertainty, 4, 5–28.CrossRefGoogle Scholar
Howrey, E. P., Klein, L. R. and McCarthy, M. D. (1974), Notes on testing the predictive performance of econometric models, International Economic Review, 15, 366–83.CrossRefGoogle Scholar
Huss, W. R. (1986), Comparative analysis of company forecasts and advanced time-series techniques using annual electric utility energy sales data, International Journal of Forecasting, 1, 217–39.CrossRefGoogle Scholar
Hyndman, R. J. (2013), Forecasting without forecasters, Keynote lecture at the 2013 International Symposium on Forecasting, Seoul, Korea.
Kahneman, D. (2012), Thinking, Fast and Slow, London: Penguin.Google Scholar
Kliger, D. and Levy, O. (2010), Overconfident investors and probability misjudgements, Journal of Socio-Economics, 39, 24–9.CrossRefGoogle Scholar
Kranendonk, H., de Jong, J. and Verbruggen, J. (2009), Accuracy of CPB forecasts 1971–2007, CPB Memorandum 178, Netherlands Bureau of Economic Policy Analysis.
Kranendonk, H. and Verbruggen, J. (2007), SAFFIER, a multi-purpose model of the Dutch economy for short-term and medium-term analyses, CPB Document 144.
Lamont, O. A. (2002), Macroeconomic forecasts and microeconomic forecasters, Journal of Economic Behavior and Organization, 48, 265–80.CrossRefGoogle Scholar
Lawrence, M., Goodwin, P., O’Connor, M. and Önkal, D. (2006), Judgemental forecasting: A review of progress over the last 25 years, International Journal of Forecasting, 22, 493–518.CrossRefGoogle Scholar
Ledolter, J. (1989), The effect of additive outliers on the forecasts from ARIMA models, International Journal of Forecasting, 5, 231–40.CrossRefGoogle Scholar
Legerstee, R. and Franses, P. H. (2007), Competence and confidence effects in experts’ forecast adjustments, Econometric Institute Report 2007–8–30, Erasmus School of Economics.
Legerstee, R. and Franses, P. H. (2014), Do experts’ SKU forecasts improve after feedback?Journal of Forecasting, 33, 69–79.CrossRefGoogle Scholar
Legerstee, R., Franses, P. H. and Paap, R. (2011), Do experts incorporate statistical model forecasts and should they?, Tinbergen Institute Discussion Paper, 11–154/4, Erasmus University, Rotterdam.
Makridakis, S. and Hibon, M. (2000), The M3-competition: Results, conclusions and implications, International Journal of Forecasting, 16, 451–76.CrossRefGoogle Scholar
Mathews, B. and Diamantopoulos, A. (1986), Managerial intervention in forecasting: An empirical investigation of forecast manipulation, International Journal of Research in Marketing, 3, 3–10.CrossRefGoogle Scholar
Mathews, B. and Diamantopoulos, A. (1989), Judgmental revision of statistical forecasts: A longitudinal extension. Journal of Forecasting, 8, 129–40.CrossRefGoogle Scholar
McNees, R. K. (1990), The role of judgment in macroeconomic forecast accuracy, International Journal of Forecasting, 6, 287–99.CrossRefGoogle Scholar
Nordhaus, W. D. (1987), Forecasting efficiency: Concepts and application, Review of Economics and Statistics, 69, 667–74.CrossRefGoogle Scholar
Patton, A. J. and Timmermann, A. (2007a), Properties of optimal forecasts under asymmetric loss and nonlinearity, Journal of Econometrics, 140, 884–918.CrossRefGoogle Scholar
Patton, A. J. and Timmermann, A. (2007b), Testing forecast optimality under unknown loss, Journal of the American Statistical Association, 102, 1172–84.CrossRefGoogle Scholar
Powell, M. and Ansic, D. (1997), Gender differences in risk behaviour in financial decision-making: An experimental analysis, Journal of Economic Psychology, 18, 605–28.CrossRefGoogle Scholar
Rabin, M. (2002), Inference by believers in the Law of Small Numbers, Quarterly Journal of Economics, 117, 775–816.CrossRefGoogle Scholar
Remus, W. E.O’Connor, M. J. and Griggs, K. (1996), Does feedback improve the accuracy of recurrent judgmental forecasts?, Organizational Behavior and Human Decision Processes, 66, 22–30.CrossRefGoogle Scholar
Sanders, N. R. (1992), Accuracy of judgmental forecasts: A comparison, Omega, 20, 353–364.CrossRefGoogle Scholar
Sanders, N. R. (1997), The impact of task properties feedback on time series judgmental forecasting tasks, Omega: The International Journal of Management Science, 25, 135–144.CrossRefGoogle Scholar
Sanders, N. R. and Ritzman, L. (2001), Judgemental adjustments of statistical forecasts, in Armstrong, J. S. (ed.), Principles of Forecasting, New York: Kluwer.Google Scholar
Shanteau, J. (1992), The psychology of experts: An alternative view, in Wright, G. and Bolger, F. (eds.), Expertise and Decision Support, New York: Plenum, pp. 11–23.CrossRefGoogle Scholar
Simon, H. A. (1992), What is an explanation of behavior?, Psychological Science, 3, 150–61.CrossRefGoogle Scholar
Singh-Manoux, A., Kivimaki, M., Glymour, M., Elbaz, A., Berr, C., Ebmeier, K. B., Ferrie, J. E. and Dugravot, A. (2012), Timing of onset of cognitive decline: Results from Whitehall II prospective cohort study, British Medical Journal, 344, d7622.CrossRefGoogle ScholarPubMed
Stekler, H. O. (2007), The future of macroeconomic forecasting: Understanding the forecasting process, International Journal of Forecasting, 23, 237–48.CrossRefGoogle Scholar
Stone, E. R. and Opel, R. B. (2000), Training to improve calibration and discrimination: The effects of performance and environmental feedback, Organizational Behavior and Human Decision Processes, 83, 282–309.CrossRefGoogle ScholarPubMed
Taleb, N. N. (2007), The Black Swan: The Impact of the Highly Improbable, New York: Random House.Google Scholar
Tetlock, P. E. (2005), Expert Political Judgment: How Good is It? How Can We Know?, Princeton University Press.Google Scholar
Timmermann, A. (2006), Forecast combinations, in Elliott, G., Granger, C. W. J. and Timmermann, A. (eds.), Handbook of Economic Forecasting, Vol. I, Amsterdam: Elsevier, pp. 135–96.CrossRefGoogle Scholar
Tsay, R. S. (1988), Outliers, level shifts, and variance changes in time series, Journal of Forecasting, 7, 1–20.CrossRefGoogle Scholar
Turner, D. S. (1990), The role of judgment in macroeconomic forecasting, Journal of Forecasting, 9, 319–45.CrossRefGoogle Scholar
Tversky, A. and Kahneman, D. (1971), Belief in the Law of Small Numbers, Psychological Bulletin, 76, 105–10.CrossRefGoogle Scholar
Varian, H. (1975), A Bayesian approach to real estate assessment, in Fienberg, S. and Zellner, A. (eds.), Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage, Amsterdam: North Holland, pp. 195–208.Google Scholar
Welch, E., Bretschneider, S. and Rohrbaugh, J. (1998), Accuracy of judgmental extrapolation of time series data: Characteristics, causes, and remediation strategies for forecasting, International Journal of Forecasting, 14, 95–110.CrossRefGoogle Scholar
West, K. D. (1996), Asymptotic inference about predictive ability, Econometrica, 64, 1067–84.CrossRefGoogle Scholar
Willemain, T. R. (1989), Graphical adjustment of statistical forecasts, International Journal of Forecasting, 5, 179–85.CrossRefGoogle Scholar
Yaniv, I. and Kleinberger, E. (2000), Advice taking in decision making: egocentric discounting and reputation formation, Organizational Behavior and Human Decision Processes, 83, 260–81.CrossRefGoogle ScholarPubMed
Zellner, A. (1986), Bayesian estimation and prediction under asymmetric loss functions, Journal of the American Statistical Association, 81, 446–51.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Philip Hans Franses, Erasmus Universiteit Rotterdam
  • Book: Expert Adjustments of Model Forecasts
  • Online publication: 05 October 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139963329.009
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Philip Hans Franses, Erasmus Universiteit Rotterdam
  • Book: Expert Adjustments of Model Forecasts
  • Online publication: 05 October 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139963329.009
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Philip Hans Franses, Erasmus Universiteit Rotterdam
  • Book: Expert Adjustments of Model Forecasts
  • Online publication: 05 October 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139963329.009
Available formats
×