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Bretschneider Bretschneider, Carbone Carbone, Longini Longini (1979)
An adaptive approach to time series analysisDecision Sciences, 10
H. Nelson, C. Granger (1979)
Experience with using the Box-Cox transformation when forecasting economic time seriesJournal of Econometrics, 10
(1980)
VNIAEP Program Documentation
J. Bates, C. Granger (1969)
The Combination of ForecastsJournal of the Operational Research Society, 20
Spyros Makridakis, M. Hibon (1979)
Accuracy of Forecasting: An Empirical Investigation, 142
E. Parzen (1982)
ARARMA models for time series analysis and forecastingJournal of Forecasting, 1
J. Armstrong (1978)
Forecasting with Econometric Methods: Folklore Versus Fact
Bates Bates, Granger Granger (1969)
Combination of forecastsOperational Research Quarterly, 20
P. Slovic (1972)
Psychological Study of Human Judgment: Implications for Investment Decision-MakingJournal of Finance, 27
S. Bretschneider, R. Carbone, R. Longini (1979)
AN ADAPTIVE APPROACH TO TIME‐SERIES FORECASTINGDecision Sciences, 10
P. Harrison, C. Stevens (1971)
A Bayesian Approach to Short-term ForecastingJournal of the Operational Research Society, 22
(1916)
J.and Stevens, C.'Bayesian forecasting
S. Taylor (1979)
Forecasting Economic Time SeriesJournal of the Operational Research Society, 30
M. Hollander, D. Wolfe, E. Chicken (1973)
Nonparametric Statistical Methods: Hollander/Nonparametric Statistical Methods
Michele Hibon is currently a research assistant at INSEAD. For the last several years she has been working on various studies dealing with forecasting accuracy of time-series methods
Makridakis Makridakis, Hibon Hibon (1979)
Accuracy of forecasting: an empirical investigation (with discussion)Journal of the Royal Statistical Society. (A)., 142
P. Newbold, C. Granger (1974)
Experience with Forecasting Univariate Time Series and the Combination of Forecasts, 137
Spyros Makridakis, S. Wheelwright, Victor McGee (1979)
Forecasting: Methods and Applications
G. Box, G. Jenkins, G. Reinsel, G. Ljung (1978)
Time Series Analysis: Forecasting and ControlThe Statistician, 27
R. Carbone, R. Longini (1977)
A Feedback Model for Automated Real Estate AssessmentManagement Science, 24
His Ph.D. is in urban and public affairs from Carnegie-Mellon University. He is a member of AIDS, TIMS, and ORSA. His papers have appeared in The Journal of Environmental Systems, Management Science
D. Hsu, O. Anderson (1980)
Time Series Analysis and Forecasting: The Box-Jenkins Approach.Journal of the American Statistical Association, 75
(1969)
Slovic
R. Winkler (1981)
Combining Probability Distributions from Dependent Information SourcesManagement Science, 27
(1980)
The role of linear recursive estimates in time series forecasting'. Computer Sciences Division, Union Carbide Corporation (Nuclear Division)
Harrison Harrison, Stevens Stevens (1976)
Bayesian forecastingJournal of the Royal Statistical Society, (B), 38
E. Gardner, D. Dannenbring (1980)
FORECASTING WITH EXPONENTIAL SMOOTHING: SOME GUIDELINES FOR MODEL SELECTIONDecision Sciences, 11
(1973)
The Accuracy oj Major Extrapolation {Time Series
Harrison Harrison, Stevens Stevens (1971)
A Bayesian approach to short‐term forecastingOperational Research Quarterly, 22
E. Parzen (1979)
Time Series Modeling, Spectral Analysis, and Forecasting.
Allan Andersen completed a Ph.D. in the Faculty of Economics at the University of Queensland
(1972)
He has published extensively in the areas of general systems and forecasting and has co-authored Computer-Aided Modeling for Managers
(1969)
A comparative study of time series prediction techniques on economic data
C. Chatfield, D. Prothero (1973)
Box‐Jenkins Seasonal Forecasting: Problems in a Case‐Study, 136
(1979)
Time series and whitening filter estimation
N. Ghosh (1976)
Time Series Analysis and Forecasting (the Box-Jenkins Approach)Journal of the Operational Research Society, 27
F. Maxwell, C. Nelson (1973)
Applied Time Series Analysis for Managerial Forecasting.Biometrics, 30
In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.
Journal of Forecasting – Wiley
Published: Apr 1, 1982
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