In This Article Expand or collapse the "in this article" section Forecasting

  • Introduction
  • Textbooks, Reference Books, and Overviews
  • Journals
  • Forecasting Software and Support Systems
  • General Articles and Commentaries
  • Nonlinear Computer-Intensive Methods, Artificial Intelligence, and Machine Learning
  • Forecasting Distributions
  • Forecasters and Forecasting Practice

Management Forecasting
Robert Fildes, P. Geoffrey Allen
  • LAST REVIEWED: 20 January 2023
  • LAST MODIFIED: 26 June 2019
  • DOI: 10.1093/obo/9780199846740-0064


The history of forecasting goes back at least as far as the Oracle at Delphi in Greece. Stripped of its mystique, this was what we now refer to as “unaided judgment,” the only forecasting method available for centuries. As a formal area of study, the earliest examples are from the weather forecasters of the 19th century. The early years of the 20th century saw increasing interest in business and economic forecasting, which is the focus of this article. Novel methods were applied to agricultural yields and prices as well as analysis of the business cycle. Researchers became interested in both methods and in producers of forecasts who may or may not use formal methods. Construction of macroeconomic models occurred in the 1940s associated with the work of the Cowles Commission. As the first computers became available, these models were estimated and used for forecasting. The 1970s marked the era of univariate forecasting; ARIMA modeling and exponential smoothing both date from this time and are widely used today in business forecasting. The 1980s saw a further institutionalization of the subject and increasing exchange between the different groups of researchers, economists, statisticians, engineers and, later, psychologists. The Journal of Forecasting and subsequently the International Journal of Forecasting were founded; both aimed to integrate the disparate aspects of forecasting. It was also the era of some major forecasting developments: unit-root testing, vector autoregression, cointegration, state-space modeling, and ARCH modeling. Questions about the best forecasting method were tested in “competitions” between methods, but clear answers were not forthcoming. As computing power has continued to increase, more sophisticated and complex forecasting methods have emerged, in particular the methods of neural networks. As to the future of forecasting, it seems a safe bet that we will see computing power used to handle more data, more nonlinear methods, more emphasis on forecast distributions and perhaps more on the limitations of business and economic forecasting and the strategies that should be followed. After introducing books and papers that examine the breadth of forecasting, this bibliography’s structure recognizes that the fundamental methods of business and economic forecasting—judgment, extrapolative time series methods, and econometrics—still have distinct development paths. The newer area of computationally intensive methods, artificial intelligence and machine learning, often applied to predicting individual consumer behavior, is included separately. Forecasting software is also surveyed briefly, as its dissemination has been critical both to research innovation and to changes in forecasting practice. Finally, the bibliography covers various important areas of application.

Textbooks, Reference Books, and Overviews

Books on forecasting range widely: from the specialist and historical to popular overviews of the subject such as The Signal and the Noise (Silver 2012). Silver’s expertise in US election forecasting generated massive publicity as to the importance of identifying the useful information in data (the signal) and eliminating randomness (noise). Effectively the book proposes an argument that valuable forecasts are best produced through the careful analysis and modeling of data. Undergraduate- or graduate-level textbooks come in two categories: generalist and those that focus primarily on a particular set of methods such as time series. Generalist studies include Makridakis, et al. 1998, as well as Hyndman and Athanasopoulos 2018; Gonzalez-Rivera 2016; and Ord, et al. 2017. Diebold 2006 offers a more theoretical perspective. Armstrong 2001 is a collection of chapters written by leading forecasters, built around a set of principles and intended as a reference guide to both researchers and practitioners. Allen and Fildes 2011 contains many of the seminal articles in the field.

  • Allen, P. G., and R. Fildes, eds. Handbook of Business and Economic Forecasting. 5 vols. London: SAGE, 2011.

    Contains seventy-seven reprints published from 1960 onward, covering much the same range as this bibliography. Includes a historical introduction to the subject and its various subfields.

  • Armstrong, J. Scott, ed. Principles of Forecasting: A Handbook for Researchers and Practitioners. New York: Springer Science + Business Media, 2001.

    DOI: 10.1007/978-0-306-47630-3

    Consists of thirty chapters by some forty different authors covering all aspects of qualitative and quantitative forecasting, reviewing methods, evaluation, and applications mostly written in a non-technical style. Built around a set of principles, usually supported with evidence of their effectiveness. The principles, and any new additions, are listed on the companion website online. A core reference book.

  • Diebold, Francis X. Elements of Forecasting. 4th ed. Mason, OH: Cengage Learning, 2006.

    Advanced undergraduate level, requiring some background in statistics. Covers graphic analysis, various univariate methods, cycles, some limited discussion of regression and the more advanced topics of unit roots and volatility measurement. Includes instructor resources. “Retired” by author though can be found online.

  • Gonzalez-Rivera, Gloria. Forecasting for Economics and Business. Oxford and New York: Routledge, 2016.

    Intended for juniors/seniors in economics and business majors and others who have a basic background in algebra, statistics, and linear regression. Emphasis on real-world examples and data. (First published by Pearson, 2013.)

  • Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice. 2d ed. Heathmont, Australia: OTexts, 2018.

    Available online and as a textbook. For undergraduates and practitioners with knowledge of basic algebra. Many real-world examples are given. Uses R for analysis and emphasizes graphical presentation. Covers judgmental forecasting, regression, time-series decomposition, smoothing, ARIMA models, and advanced forecasting methods.

  • Makridakis, Spyros, Stephen C. Wheelwright, and Rob J. Hyndman. Forecasting: Methods and Applications. 3d ed. New York: Wiley, 1998.

    One of the first books to cover forecasting as it is practiced. Does not require much statistical background beyond an introductory course. Not updated and now rather dated.

  • Ord, Keith, Robert Fildes, and Nikolaos Kourenzes. Principles of Business Forecasting. 2d ed. New York: Wessex, 2017.

    For undergraduates, specialist masters, and practitioners. Requires limited statistics background. General principles guide and simplify forecasting practice. Uses real data sets and a variety of software (including extensive use of R). Covers standard forecasting methods, including judgmental methods. Discusses forecasting applications in operations and marketing, organizing forecasting support systems, and dealing with uncertainty. Includes instructor resources.

  • Silver, Nate. The Signal and the Noise: The Art and Science of Prediction. New York and London: Penguin, 2012.

    An entertaining introduction to various important topics in forecasting, many of which Silver has worked on, including elections, poker, weather, climate and economics. Contains many sensible principles but no modeling; in essence it is an argument for careful data analysis and modeling.

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