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Using data on previous forecasts, this widely influential 1995 FT article criticizes the reliability and usefulness of economic forecasts. Contrary to the widespread belief that economists always disagree, it shows that economic forecasts typically fall into quite a narrow range, but that the range rarely includes the outcome.

Econometric forecasters continually seek ways to increase forecast accuracy. As new data are released, the residuals of forecasting models are examined for evidence of structural change and equations are modified if necessary. Several of the participants in the Model Comparison Seminar have recently investigated alternative methods for using monthly data in a systematic way to adjust forecasts produced by quarterly models. These initial studies are reviewed in this chapter and some illustrative results are presented. It begins with a review of some of the implications of temporal aggregation for the specification and estimation of models and their use in economic forecasting. This review is intended to provide motivation for the use of high-frequency (monthly) data in forecasting economic aggregates, as well as to indicate some of the difficulties that are involved. The chapter concludes with a presentation of some illustrative results obtained using the Michigan Quarterly Econometric Model of the United States economy.

Economic forecasts differ because forecasters use different macroeconomic models. However, even if everyone used the same model, all forecasts would not be identical. Most forecasts reflect a complex interaction among three elements. Unfortunately, little is known about the relative importance of these elements. This chapter addresses three kinds of question. The initial stage of the Model Comparison Seminar's project, starting in early 1986, has been the collection of relevant data, a laborious and time-consuming part of the project. The following results are a preliminary report on an ongoing effort. The conclusions, based on the limited experience so far, must be regarded as highly tentative. Any success that has been achieved should be largely credited to the modelers who participated in this exercise. This chapter compares model solutions based on different sets of conditioning information. In general, a model can be thought of as a conditional statement about the relationship between inputs (Xs) and outputs (Ys), or Y = f(X).

In Japan, the general context of budget-making, up to the Cabinet's approval of the Ministry of Finance's draft budgets for both the General Account Budget and the Fiscal Investment Loan Programme in December, was shaped by a combination of short-term and medium-term political and economic factors. The medium-term context was set by the five-year National Economic Plan and the Medium Term Fiscal Projection. Revised and rolled forward each year, the latter provided stylized projections of aggregate expenditures and revenues for the upcoming fiscal year and three years ahead, based mainly on the (equally stylized) economic growth assumptions in the National Economic Plan. The shorter-term macroeconomic context was provided mainly by the annual estimates of GDP (and other key economic variables), and the consequential projections of revenue and borrowing.

A major purpose of nonlinear models is forecasting. This often requires numerical techniques, and a few alternatives are considered. There are also cases in which analytical forecasts are available, and some of them are shown as examples. Measuring forecast accuracy is given some consideration. This includes comparing forecasts from different models using statistical tests. Published studies comparing between forecasts from linear and nonlinear models in order to assess the usefulness of nonlinear models are discussed.

The Model Comparison Seminar has produced some striking findings. The differences in the properties of the major macroeconometric models are much bigger than one might have expected given their emphasized theoretical foundations, or given the similarity of the published economic forecasts. F. Gerard Adams and Lawrence Klein conclude that there is “considerable, one might even say astonishing,” variation in the behavior of models “largely intended for the same purposes.” Roger Brinner and Alert Hirsch refer to these sharp differences as “disturbing, particularly insofar as models share a common theoretical basis.” Stephen McNees shows that the models themselves agree much less on forecasts than do the forecasters: the forecasts of the exogenous policy variables and the ad hoc adjustments serve to make the different models' forecasts much more similar than they would be if the models alone accounted for the differences in forecasts.

This chapter compares the predictive accuracy of the Michigan and Fair econometric models using the method developed in Ray Fair. These models are compared to each other and to an eighth-order autoregressive model. The method accounts for the four main sources of uncertainty of an economic forecast: uncertainty due to the error terms, the coefficient estimates, the exogenous variables, and the possible misspecification of the model. Because it accounts for these four sources, it can be used to make comparisons across models. The method has been used to compare the Fair model to autoregressive models, vector autoregressive models, Thomas Sargent's classical macroeconomic model, and a small linear model, but this is the first time it has been used to compare two relatively large structural models. The chapter's primary aim is to demonstrate the application of the comparison method to large models.

How do economic forecasters produce legitimate and credible predictions of the economic future, despite most of the economy being transmutable and indeterminate? Using data from a case study of economic forecasting institutes in Germany, this chapter argues that the production of credible economic futures depends on an epistemic process embedded in various forms of interaction. This interactional foundation—through ‘foretalk’ and ‘epistemic participation’ in networks of internal and external interlocutors—sharpens economic forecasts in three ways. First, it brings to light new imaginaries of the economic future, allowing forecasters to spot emerging developments they would otherwise have missed. Second, it ensures the forecasts’ social legitimacy. And finally, it increases the forecasts’ epistemic quality by providing decentralized information about the intentions and assumptions of key economic and political actors.

This chapter focuses on the response of the various econometric models to a number of alternatively specified external shocks. Altogether, 11 model groups supplied simulation results for the model comparisons. These groups represent a cross section of the currently active model operators and forecasters. They run from the now traditional Keynesian to the monetarist and the rational expectations approaches. The participating model groups and the special characteristics of their models are listed in the chapter. Early in the meetings of the model comparison seminar, the philosophy of model comparisons was a topic of some extensive discussion. The approach was to compare alternative “disturbed” model solutions to a base solution. Each model operator was instructed to prepare a so-called “tracking solution” that would approximately reproduce history over the period 1975 to 1984.

The first part of this chapter explains why economics has enjoyed more success in answering microeconomic questions rather than macroeconomic ones. The second part responds to a number of attacks on economics, by clarifying that the foremost role of economics is to clarify our understanding and organize our thoughts rather than to help us make forecasts.

Building bigger and more elaborate models will neither help us arrive at more realistic descriptions of the world nor help make our predictions more accurate. Mathematical models are just a more accurate way of describing certain complex systems and even though such a model can enhance our understanding of the problem and help us check the internal consistency of our assumptions, it will not tell us something we have not already told it.

This chapter examines the economic forecasting records of Milton Friedman and Paul Samuelson for the US economy during the five years from 1970 to 1974, a period chiefly characterized by Nixonomics. The main sources employed for carrying out this study are threefold, namely the underused Economics Cassette Series, the many Newsweek columns written by Friedman and Samuelson, and occasional pieces contributed by Samuelson for the Financial Times. Although we look at both quantitative and qualitative forecasts, the focus is on quantitative predictions for real GNP growth, inflation, and unemployment. In quantitative terms, we find that Samuelson’s projections were more accurate during the period considered, albeit subject to various caveats.

This chapter focuses on long-range economic forecasts and ideas about predictability in the work of commodity price forecaster Samuel Benner, utopian novelist Edward Bellamy, and meteorologist Henry Helm Clayton. It begins by examining the theme of predictive certainty in Samuel Benner’s popular guide to commodity price cycles, Benner’s Prophecies of Future Ups and Downs in Prices, which first appeared in 1876 and was reprinted in over fifteen editions until 1908. The chapter then explores the theme of foresight in the literary work of Edward Bellamy, emphasizing the economic certainties underpinning the futuristic vision of utopian socialism in his best-selling 1888 novel Looking Backward. Finally, the chapter traces the career of Henry Helm Clayton from his tenure at Massachusetts’ Blue Hill Meteorological Observatory from 1886 to 1909 through the operation of his private consulting weather service from 1925 to 1942, throughout which time he sought to establish a correlation between meteorological and economic cycles and to popularize his theories of periodicity. This chapter demonstrates the enduring appeal of predictive certainties in the realm of the market and political economy despite the increasing acceptance of probabilism in science and culture at the turn of the twentieth century.

Between 1999 and 2007, global economic trends were characterized by the aftermath of the Asian financial crisis, the bubble and burst of the dot-com crisis, and a global recovery which triggered a boom in commodity prices and inflated housing and stock market bubbles in the US and other high-income countries. These trends culminated into the global financial crisis of 2008–9. Few observers saw this crisis coming. UN economists did. This chapter looks at what UN economists saw as risks, but other observers failed to see: (a) the widening savings–investment imbalances across countries; (b) the risks of cheap money in the context of unregulated finance; (c) the decoupling of economic growth between developed and developing countries; and (d) the risk of imminent exchange rate instability. Revisiting the history of this period is relevant for drawing lessons for the understanding of today’s buildup of another dot-com bubble and its possible consequences.

This chapter examines macroeconomic forecasts for South Africa up to the year 2025. The uncertainties governing economic projections, particularly over longer-term horizons, motivate the specification of a number of alternative scenarios rather than just a simple baseline forecast. Three alternative macroeconomic scenarios over the period 2017–25 are considered. The baseline scenario assumes that both the global and the South African economies will continue to recover from the global financial crisis and the Great Recession. The low-road scenario assumes that international conditions improve less and domestic political and labour relation conditions improve very little from current positions. The high-road scenario is based on both improved (relative to the baseline view) global economic climate and a much better domestic political and economic policy context.

This brief chapter, written in the backdrop of the global financial crisis and the start of the Great Recession, lays out a broad philosophical approach to dealing with policy failures and the need for economists as a profession to introspect. It emphasizes the need for scepticism, in all our contemplation about the world, a philosophical approach that underlies a lot of what follow in this book.

This chapter predicts that China is heading towards an investment bust from which its financial system is unlikely to escape unscathed. Consumption is expected to hold up rather well through this process, but it will not accelerate by the 1.5 percentage points needed to maintain steady headline GDP growth for every percentage point that investment is expected to slow. The labour market will be hit by a slowdown in construction activity, and high-net worth households are likely to face negative wealth effects from falling property values. While government policies and increased subsidies may be able to offset some of these effects, Chinese GDP growth is expected to slip to 4–6 per cent at some point, not too long after 2013.