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Review Of Daniel Kahnemann, Paul Slovic, And Amos Tversky (Eds.), Judgment Under Uncertainty: Heuristics And Biases, J. Scott Armstrong
Review Of Daniel Kahnemann, Paul Slovic, And Amos Tversky (Eds.), Judgment Under Uncertainty: Heuristics And Biases, J. Scott Armstrong
J. Scott Armstrong
This book provides a convenient collection of important papers relevant to a subset of judgmental forecasting. My review discusses: (i) the scope of the readings (ii) the importance of the readings (iii) what is new (iv) how the book is organized (v) advice on using the book, and (vi) who should read the book.
A Comparative Study Of Methods For Long-Range Market Forecasting, J. Scott Armstrong, Michael C. Grohman
A Comparative Study Of Methods For Long-Range Market Forecasting, J. Scott Armstrong, Michael C. Grohman
J. Scott Armstrong
The following hypotheses about long-range market forecasting were examined: Hl Objective methods provide more accuracy than do subjective methods. H2 The relative advantage of objective over subjective methods increases as the amount of change in the environment increases. H3 Causal methods provide more accuracy than do naive methods. H4 The relative advantage of causal over naive methods increases as the amount of change in the environment increases. Support for these hypotheses was then obtained from the literature and from a study of a single market. The study used three different models to make ex ante forecasts of the U.S. air …
Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons, J. Scott Armstrong, Fred Collopy
Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons, J. Scott Armstrong, Fred Collopy
J. Scott Armstrong
This study evaluated measures for making comparisons of errors across time series. We analyzed 90 annual and 101 quarterly economic time series. We judged error measures on reliability, construct validity, sensitivity to small changes, protection against outliers, and their relationship to decision making. The results lead us to recommend the Geometric Mean of the Relative Absolute Error (GMRAE) when the task involves calibrating a model for a set of time series. The GMRAE compares the absolute error of a given method to that from the random walk forecast. For selecting the most accurate methods, we recommend the Median RAE (MdRAE)when …