On the Nature of the Nonexistence of Ordinal Relationships between Measures of the Accuracy and Value of Probability Forecasts : An Example

1977 ◽  
Vol 16 (10) ◽  
pp. 1015-1021 ◽  
Author(s):  
Allan H. Murphy ◽  
Jack C. Thompson
Keyword(s):  
Decision Making ◽  
Forecast Accuracy ◽  
Existence And Nonexistence ◽  
Fruit Orchard ◽  
Probability Forecasts

Abstract t has been shown previously that ordinal relationships between measures of the accuracy and value ofprobability forecasts do not exist, in general, in N-state (N > 2) situations. Some implications of this resultare illustrated by comparing the accuracy and value of such forecasts in a realistic decision-making situation-a three-action, three-state situation involving the protection of a fruit orchard against frosts and freezes.Geometrical interpretations of the forecasts and measures are described and then used to investigate the existence of ordinal relationships in this so-called fruit-frost situation. The results indicate, as expected, thatan increase in forecast accuracy can lead to a decrease in forecast value. Some generalizations and speculations related to the existence and nonexistence of such ordinal relationships are presented.

scholarly journals Forecasting and Analysis Tools for Regional Industries’ Dynamics

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Valeriy Semenychev ◽  
Anastasiya Korobetskaya
Keyword(s):  
Decision Making ◽  
Evolutionary Dynamics ◽  
Forecast Accuracy ◽  
High Accuracy ◽  
General Idea ◽  
Russian Regions ◽  
Seasonal Component ◽  
Modeling Analysis ◽  
Splitting Time ◽  
Regions Of Russia

The article is devoted to the author’s approach and tools for regional industries’ modeling, analysis and forecasting, following the general idea of splitting time series into four components: trend, cycles, seasonal component, and residuals. However, the authors introduce new approaches, models, metrics, and identification algorithms, and the components’ interaction structures, having included the analysis of 12 industries in 82 regions of Russia. The models and forecast accuracy were tested on 3–12 month forecasts, thus proving their high accuracy. Therefore, the article proposes not only new systematic econometric tools but a methodology for decision making, developed to provide stable and adequate characteristics of complex non-linear evolutionary dynamics of Russian regions.

Decision Making with Probability Forecasts of Rainfall

1989 ◽  
Vol 64 (3_suppl) ◽  
pp. 1051-1055
Author(s):  
Yoshiaki Nakajima ◽  
Hirohiko Ohta
Keyword(s):  
College Students ◽  
Decision Making ◽  
Subjective Probability ◽  
Weather Condition ◽  
Probability Forecasts

Decision making with probability forecasts of rainfall was investigated experimentally by considering three variables, i.e., the rainfall probability, the duration of being away from home, and the outdoor weather condition. 274 Japanese college students were asked to make decisions on the necessity for person A to take an umbrella with him under 16 different conditions. Analysis showed obviously that the necessity tended to increase as probability of rainfall rose, as duration became longer, and also as the outdoor weather was cloudy rather than clear. Discussion of these results considered recent studies of decision making and subjective probability.

Perceived links between climate change and weather forecast accuracy: new barriers to tools for agricultural decision-making

2021 ◽  
Vol 168 (1-2) ◽  
Author(s):  
Zack Guido ◽  
Sara Lopus ◽  
Kurt Waldman ◽  
Corrie Hannah ◽  
Andrew Zimmer ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Forecast Accuracy ◽  
Weather Forecast

scholarly journals Forecasting Agricultural Prices Using a Bayesian Composite Approach

1988 ◽  
Vol 20 (2) ◽  
pp. 73-80 ◽  
Author(s):  
Christopher S. McIntosh ◽  
David A. Bessler
Keyword(s):  
Decision Making ◽  
Least Squares ◽  
Bayesian Approach ◽  
Forecast Accuracy ◽  
Agricultural Prices ◽  
Adaptive Weighting ◽  
Composite Approach ◽  
The Matrix ◽  
Weighting Technique ◽  
Quality Forecast

AbstractForecast users and market analysts need quality forecast information to improve their decision-making abilities. When more than one forecast is available, the analyst can improve forecast accuracy by using a composite forecast. One of several approaches to forming composite forecasts is a Bayesian approach using matrix beta priors. This paper explains the matrix beta approach and applies it to three individual forecasts of U.S. hog prices. The Bayesian composite forecast is evaluated relative to composites made from simple averages, restricted least squares, and an adaptive weighting technique.

scholarly journals Decision Fatigue and Heuristic Analyst Forecasts

2018 ◽  
Author(s):  
Yaron Levi ◽  
David Hirshleifer ◽  
Siew Hong Teoh ◽  
Ben Lourie
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Forecast Accuracy ◽  
Analyst Forecasts ◽  
Decision Quality ◽  
Psychological Evidence

Psychological evidence indicates that decision quality declines after an extensive session of decision-making, a phenomenon known as decision fatigue. We study whether decision fatigue affects analysts’ judgments. Analysts cover multiple firms and often issue several forecasts in a single day. We find that forecast accuracy declines over the course of a day as the number of forecasts the analyst has already issued increases. Also consistent with decision fatigue, we find that the more forecasts an analyst issues, the higher the likelihood the analyst resorts to more heuristic decisions by herding more closely with the consensus forecast, by self-herding (i.e., reissuing their own previous outstanding forecasts), and by issuing a rounded forecast. Finally, we find that the stock market understands these effects and discounts for analyst decision fatigue.

scholarly journals Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 436 ◽  
Author(s):  
José Manuel Oliveira ◽  
Patrícia Ramos
Keyword(s):  
Decision Making ◽  
Forecast Accuracy ◽  
Real Data ◽  
Information Criteria ◽  
State Space Models ◽  
Retail Sector ◽  
Forecasting Method ◽  
The Individual ◽  
Different Levels ◽  
Reconciliation Process

Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7% and 3.7% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.

Sophistication-Related Differences in Investors' Models of the Relative Accuracy of Analysts' Forecast Revisions

2003 ◽  
Vol 78 (3) ◽  
pp. 679-706 ◽  
Author(s):  
Sarah E. Bonner ◽  
Beverly R. Walther ◽  
Susan M. Young
Keyword(s):  
Decision Making ◽  
Methodological Approach ◽  
Forecast Accuracy ◽  
Judgment And Decision Making ◽  
Relative Accuracy ◽  
Lens Model ◽  
Forecast Revisions ◽  
Number Of Factors ◽  
Specific Factors

The accuracy of sell-side analysts' forecast revisions is related to a number of factors, including characteristics of the analyst and the age of the forecast. In this study we examine whether there are differences in how sophisticated and unsophisticated investors use these factors to predict the relative accuracy of forecast revisions. We adapt the lens model methodological approach from the judgment and decision-making literature to investigate these differences in an archival setting. Our results suggest that sophisticated investors have greater knowledge overall about the relation of the factors to forecast accuracy. Further, our evidence is consistent with sophisticated investors relying more on the specific factors that provide the most benefits (relative to their costs) for predicting relative forecast accuracy.

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