Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Publication
- Publication Type
Articles 1 - 27 of 27
Full-Text Articles in Physical Sciences and Mathematics
Forecasting By Extrapolation: Conclusions From Twenty-Five Years Of Research, J. Scott Armstrong
Forecasting By Extrapolation: Conclusions From Twenty-Five Years Of Research, J. Scott Armstrong
J. Scott Armstrong
Sophisticated extrapolation techniques have had a negligible payoff for accuracy in forecasting. As a result, major changes are proposed for the allocation of the funds for future research on extrapolation. Meanwhile, simple methods and the combination of forecasts are recommended.
Communication Of Research On Forecasting: The Journal, J. Scott Armstrong
Communication Of Research On Forecasting: The Journal, J. Scott Armstrong
J. Scott Armstrong
It seems trivial to point out that one of the major goals of the International Institute of Forecasters is to communicate research findings. In particular, the IIF tries to foster communication among researchers, between researchers and practitioners, across nationalities, and across disciplines. We have two major vehicles for this: the annual symposiums and the journal. This editorial examines the results that we have had to date with our journals.
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.
Index Methods For Forecasting: An Application To American Presidential Elections, J. Scott Armstrong, Alfred G. Cúzan
Index Methods For Forecasting: An Application To American Presidential Elections, J. Scott Armstrong, Alfred G. Cúzan
J. Scott Armstrong
Lichtman (2005) reports that the Keys model has been able to pick the winner of every presidential election since 1860, retrospectively through 1980 and prospectively from 1984-2004. Given this record, it seems sensible to examine this index method. We tested how well the Keys model predicted the winner of the popular vote, and also how closely it forecasted the actual percentage of the two-party vote going to the incumbent ticket. The index method performs well compared with regression models. It also offers the opportunity to incorporate many policy variables. Index methods can be applied to various choice problems faced by …
How To Make Better Forecasts And Decisions: Avoid Face-To-Face Meetings, J. Scott Armstrong
How To Make Better Forecasts And Decisions: Avoid Face-To-Face Meetings, J. Scott Armstrong
J. Scott Armstrong
When financial columnist James Surowiecki wrote The Wisdom of Crowds, he wished to explain the successes and failures of markets (an example of a "crowd") and to understand why the average opinion of a crowd is frequently more accurate than the opinions of most of its individual members. In this expanded review of the book, Scott Armstrong asks a question of immediate relevance to forecasters: Are the traditional face-to-face meetings an effective way to elicit forecasts from forecast crowds (i.e. teams)? Armstrong doesn’t believe so. Quite the contrary, he explains why he considers face-to-face meetings a detriment to good forecasting …
Introduction To Paper And Commentaries On The Delphi Technique, J. Scott Armstrong
Introduction To Paper And Commentaries On The Delphi Technique, J. Scott Armstrong
J. Scott Armstrong
Rowe and Wright’s paper "The Delphi Technique as a Forecasting Tool" was initially reviewed by four experts in the area of judgmental forecasting. Following three rounds of revisions, the paper was accepted for publication. It was then sent for commentary by Professors Ayton, Ferrell, and Stewart. The lead paper should be of interest to researchers because it identifies important aspects of the Delphi procedure that have not yet been studied. In particular, there are few validation studies and these often omit descriptions of the relevant conditions. This makes it difficult to identify which aspects of Delphi are related to accuracy …
Research Needs In Forecasting, J. Scott Armstrong
Research Needs In Forecasting, J. Scott Armstrong
J. Scott Armstrong
The demand for research on forecasting is strong. This conclusion is based on the high number of citations to papers published about research on forecasting, and upon the number of subscriptions for journals devoted to forecasting. The supply of research papers is also large, following a rapid growth in the 1960s and 1970s. This research has produced important findings. Despite this, a comparison of published research versus the needs expressed in two surveys of academics and practitioners showed that numerous gaps still exist. A review of the literature also supported this conclusion that the research being produced does not match …
Review Of Ravi Batra, The Great Depression Of 1990, J. Scott Armstrong
Review Of Ravi Batra, The Great Depression Of 1990, J. Scott Armstrong
J. Scott Armstrong
The Great Depression of 1990 was on the New York Times best-seller list for non-fiction in the summer of 1987. It follows a standard formula for best sellers in forecasting: Forecast a great disaster, and include a formula for redemption. If the disaster occurs, you can say, "I told you so." If it doesn't occur, you say, "It is good that they listened to my advice. I saved them." How can you lose? When I first saw this book, it occurred to me that it was a hoax.
Decomposition By Causal Forces: A Procedure For Forecasting Complex Time Series, J. Scott Armstrong, Fred Collopy, J. Thomas Yokum
Decomposition By Causal Forces: A Procedure For Forecasting Complex Time Series, J. Scott Armstrong, Fred Collopy, J. Thomas Yokum
J. Scott Armstrong
Causal forces are a way of summarizing forecasters' expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting causal forces; we refer to these as complex times series. It would seem that forecasting these times series would be easier if one could decompose the series to eliminate the effects of the conflicts. Given forecasts subject to high uncertainty, we hypothesized that a time series could be effectively decomposed under two conditions: …
Global Warming: Forecasts By Scientists Versus Scientific Forecasts, Kesten C. Green, J. Scott Armstrong
Global Warming: Forecasts By Scientists Versus Scientific Forecasts, Kesten C. Green, J. Scott Armstrong
J. Scott Armstrong
In 2007, the Intergovernmental Panel on Climate Changes Working Group One, a panel of experts established by the World Meteorological Organization and the United Nations Environment Programme, issued its updated, Fourth Assessment Report, forecasts. The Report was commissioned at great cost in order to provide policy recommendations to governments. It included predictions of dramatic and harmful increases in average world temperatures over the next 92 years. Using forecasting principles as our guide we asked, are these forecasts a good basis for developing public policy? Our answer is "no." To provide forecasts of climate change that are useful for policy-making, one …
Causal Forces: Structuring Knowledge For Time Series Extrapolation, J. Scott Armstrong, Fred Collopy
Causal Forces: Structuring Knowledge For Time Series Extrapolation, J. Scott Armstrong, Fred Collopy
J. Scott Armstrong
This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal …
Should We Redesign Forecasting Competitions?, J. Scott Armstrong
Should We Redesign Forecasting Competitions?, J. Scott Armstrong
J. Scott Armstrong
The M3-Competition continues to improve the design of forecasting competitions: It examines more series than any previous competition, improves error analyses and includes commercial forecasting programs as competitors. To judge where to go from here, I step back to look at the M-Competitions as a whole. I discuss the advantages of the M-Competitions in hopes that they will be retained, describe how to gain additional benefit from future competitions, and finally, describe a low-cost approach to competitions.
Findings From Evidence-Based Forecasting: Methods For Reducing Forecast Error, J. Scott Armstrong
Findings From Evidence-Based Forecasting: Methods For Reducing Forecast Error, J. Scott Armstrong
J. Scott Armstrong
Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based …
Identification Of Asymmetric Prediction Intervals Through Causal Forces, J. Scott Armstrong, Fred Collopy
Identification Of Asymmetric Prediction Intervals Through Causal Forces, J. Scott Armstrong, Fred Collopy
J. Scott Armstrong
When causal forces are specified, the expected direction of the trend can be compared with the trend based on extrapolation. Series in which the expected trend conflicts with the extrapolated trend are called contrary series. We hypothesized that contrary series would have asymmetric forecast errors, with larger errors in the direction of the expected trend. Using annual series that contained minimal information about causality, we examined 671 contrary forecasts. As expected, most (81%) of the errors were in the direction of the causal forces. Also as expected, the asymmetries were more likely for longer forecast horizons; for six-year-ahead forecasts, 89% …
Review Of Philip E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know?, Adrian E. Tschoegl, J. Scott Armstrong
Review Of Philip E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know?, Adrian E. Tschoegl, J. Scott Armstrong
J. Scott Armstrong
The book assaults common sense with evidence. In order to mount his assault on accepted wisdom, Tetlock spends some 238 pages of text explaining his methods and findings, and considering and refuting many alternative explanations, and adds some 75 pages of technical appendices. The downside of this approach is that some readers may find the book too demanding. That would be a pity as his findings are important. Tetlock’s book reports the results of a two-decade long study of expert predictions. He recruited 284 people whose professions included "commenting or offering advice on political and economic trends." He asked them …
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 …
Integration Of Statistical Methods And Judgment For Time Series Forecasting: Principles From Empirical Research, J. Scott Armstrong, Fred Collopy
Integration Of Statistical Methods And Judgment For Time Series Forecasting: Principles From Empirical Research, J. Scott Armstrong, Fred Collopy
J. Scott Armstrong
We consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can …
Forecasting With Econometric Methods: Folklore Versus Fact, J. Scott Armstrong
Forecasting With Econometric Methods: Folklore Versus Fact, J. Scott Armstrong
J. Scott Armstrong
Evidence from social psychology suggests that econometricians will avoid evidence that disconfirms their beliefs. Two beliefs of econometricians were examined: (1) Econometric methods provide more accurate short-term forecasts than do other methods; and (2) more complex econometric methods yield more accurate forecasts. A survey of 21 experts in econometrics found that 95% agreed with the first statement and 72% agreed with the second. A review of the published empirical evidence yielded little support for either of the two statements in the 41 studies. The method of multiple hypotheses was suggested as a research strategy that will lead to more effective …
Principles For Examining Predictive Validity: The Case Of Information Systems Spending Forecasts, Fred Collopy, Monica Adya, J. Scott Armstrong
Principles For Examining Predictive Validity: The Case Of Information Systems Spending Forecasts, Fred Collopy, Monica Adya, J. Scott Armstrong
J. Scott Armstrong
Research over two decades has advanced the knowledge of how to assess predictive validity. We believe this has value to information systems (IS) researchers. To demonstrate, we used a widely cited study of IS spending. In that study, price-adjusted diffusion models were proposed to explain and to forecast aggregate U.S. information systems spending. That study concluded that such models would produce more accurate forecasts than would simple linear trend extrapolation. However, one can argue that the validation procedure provided an advantage to the diffusion models. We reexamined the results using an alternative validation procedure based on three principles extracted from …
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 …
On The Selection Of Error Measures For Comparisons Among Forecasting Methods, J. Scott Armstrong, Robert Fildes
On The Selection Of Error Measures For Comparisons Among Forecasting Methods, J. Scott Armstrong, Robert Fildes
J. Scott Armstrong
Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment(GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims …
The Accuracy Of Alternative Extrapolation Models: Analysis Of A Forecasting Competition Through Open Peer Review, J. Scott Armstrong, Edward J. Lusk
The Accuracy Of Alternative Extrapolation Models: Analysis Of A Forecasting Competition Through Open Peer Review, J. Scott Armstrong, Edward J. Lusk
J. Scott Armstrong
In 1982, the Journal of Forecasting published the results of a forecasting competition organized by Spyros Makridakis (Makridakis et al., 1982). In this, the ex ante forecast errors of 21 methods were compared for forecasts of a variety of economic time series, generally using 1001 time series. Only extrapolative methods were used, as no data were available on causal variables. The accuracies of methods were compared using a variety of accuracy measures for different types of data and for varying forecast horizons. The original paper did not contain much interpretation or discussion. Partly this was by design, to be unbiased …
Forecasting Methods For Conflict Situations, J. Scott Armstrong
Forecasting Methods For Conflict Situations, J. Scott Armstrong
J. Scott Armstrong
In 1975, a consortium sponsored by the Argentine government tried to purchase the stock of the Britishowned Falkland Islands Company, a monopoly that owned 43 percent of the land in the Falklands, employed 51 per cent of the labor force, had a monopoly on all wool exports, and operated the steamship run to South America. The stockholders were willing to sell especially because the Argentine consortium was reportedly willing to pay “almost any price.” But the British government stepped in to prevent the sale, (Murray N. Rothbard, as quoted in The Wall Street Journal, 8 April 1982). In my opinion, …
The Forecasting Canon: Nine Generalizations To Improve Forecast Accuracy, J. Scott Armstrong
The Forecasting Canon: Nine Generalizations To Improve Forecast Accuracy, J. Scott Armstrong
J. Scott Armstrong
Preview: Using findings from empirically-based comparisons, Scott develops nine generalizations that can improve forecast accuracy. He finds that these are often ignored by organizations, so that attention to them offers substantial opportunities for gain. In this paper, Scott offers recommendations on how to structure a forecasting problem, how to tap managers’ knowledge, and how to select appropriate forecasting methods.
The Fertile Field Of Meta-Analysis: Cumulative Progress In Agricultural Forecasting, J. Scott Armstrong
The Fertile Field Of Meta-Analysis: Cumulative Progress In Agricultural Forecasting, J. Scott Armstrong
J. Scott Armstrong
A substantial effort has been devoted to agricultural forecasting over the past half century. Allen's quantitative review provides a powerful way to examine that research. The quantitative review (or "meta-analysis" as it is commonly called since. Glass (1976) is a formal study of studies. Meta-analyses sometimes reveal conclusions that were not obvious to those who view research findings in an impressionistic manner. Such a systematic review of the evidence should be superior to a subjective appraisal. After all, we do not trust researchers to merely look at a mass of data and decide what conclusions to draw. For those that …
The Use Of Neural Networks In The Prediction Of The Stock Exchange Of Thailand (Set) Index, Suchira Chaigusin, Chaiyaporn Chirathamjaree, Judith Clayden
The Use Of Neural Networks In The Prediction Of The Stock Exchange Of Thailand (Set) Index, Suchira Chaigusin, Chaiyaporn Chirathamjaree, Judith Clayden
Research outputs pre 2011
Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the Stock Exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks. In the experiment, many combinations of parameters were investigated to identify the right set of parameters for the neural network models in the forecasting of SET. Several global and local factors influencing the Thai stock market were used in developing the models, including the Dow …
Realized Volatility Uncertainty, David E. Allen, Michael Mcaleer, Marcel Scharth
Realized Volatility Uncertainty, David E. Allen, Michael Mcaleer, Marcel Scharth
Research outputs pre 2011
The presence of high and time-varying volatility of volatility and leverage effects bring additional uncertainty in the tails of the distribution of asset returns, even though returns standardized by (ex-post) quadratic variation measures are nearly gaussian. We argue that in this setting modeling shocks to volatility is more relevant for applications than extracting more precise predictions of the variable, as point forecasts differences are swamped by the size of the volatility of volatility and rendered less informative by the nongaussianity in the ex-ante distribution of returns. Using S&P 500 data, we document that this volatility of volatility is subject to …