Forecasting

by

Robert Fildes, P. Geoffrey Allen

• LAST REVIEWED: 11 January 2024
• LAST MODIFIED: 11 January 2024
• DOI: 10.1093/obo/9780199846740-0064

Introduction

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 nineteenth century. The early years of the twentieth 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 1960s and 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 and data scientists. 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, based on neural networks and decision trees. The 2020s has seen computing power used to handle more data, more nonlinear methods, more emphasis on forecast distributions, and perhaps more stress 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, was initially applied to predicting individual behavior and events, such as bankruptcy but now used extensively in a wide range of applications, 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 2021 and Ord, et al. 2017. The recently updated intermediate text Chatfield and Xing 2019 remains a sound treatment of time series methods. Armstrong 2001 is a collection of chapters written by leading forecasters built around a set of principles and intended as a reference guide for both researchers and practitioners. Allen and Fildes 2011 contains many of the seminal articles in the field. Recently published, Petropoulos, et al. 2022 gives an overview of many aspects of the field that is especially useful for its references.

• 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.

  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.

• Chatfield, Chris, and Haipeng Xing. The Analysis of Time Series: An Introduction with R. 7th ed. Boca Raton, FL: CRC, 2019.

  DOI: 10.1201/9781351259446

  Covers a wide range of topics, including exponential smoothing, ARIMA, state space models, forecasting, multivariate methods, and prediction intervals. Chapter on volatility added for this edition. Intermediate text.

• Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice. 3d ed. Melbourne, Australia: Otexts. 2021.

  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. Available online. Appendix suggests H. Wickham and G. Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Sebastopol, CA: O’Reilly Media, 2016) as a tutorial for learning R.

• 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.

• Petropoulos, Fotios, Danielle Apiletti, Vassilios Assimakopoulos, et al. “Forecasting: Theory and Practice.” International Journal of Forecasting 38.3 (2022): 705–871.

  DOI: 10.1016/j.ijforecast.2021.11.001

  Encyclopedic presentation of the theory and the practice of forecasting, Provides an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organize, and evaluate forecasts. Appendix B contains comprehensive list of software functions, mainly in R.

• 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 makes an argument for careful data analysis and modeling.