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About this book

Economic Forecasting provides a comprehensive overview of macroeconomic forecasting. The focus is first on a wide range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting. In addition, the book addresses the main issues surrounding the use of forecasts (accuracy, communication challenges) and their policy implications. A tour of the economic data and forecasting institutions is also provided.

Table of Contents

Frontmatter

1. First Principles

Abstract
This chapter provides an overview of the main themes covered in the book. It revolves around three basic questions:
  • First, what is being forecast? In this book, the focus is on forecasting macroeconomic variables. Aggregate changes are thus primarily of concern, with sectoral developments being considered only insofar as they have a significant impact at a more global level. These forecasts are underpinned by traditional macroeconomic analysis, especially as it relates to cyclical fluctuations and long-run growth theory (Section 1.1).
  • Second, why forecast these variables? Forecasting as a specific professional activity has emerged for both intellectual and practical reasons. The latter have become increasingly important with the growing sophistication of modern economies. Indeed, forecasts constitute a convenient framework to bring together assessments of the future and to evaluate the consequences of possible decisions (Section 1.2).
  • Third, how are forecasts produced? As a rule, a forecast combines informed judgement and model-based predictions. Well-digested information is key: the best forecaster is often the one with the most documented and sharpest reading of the facts. But the role of the model(s) is also essential: models impart a healthy dose of discipline and help draw lessons for the future from observations of the past. Finally, it is important to note that forecasting involves much more than merely coming up with a set of numbers. The forecaster must be able to explain how the numbers hang together, to weave a compelling story around them, to identify the risks surrounding the central scenario and to map out alternative courses of action (Section 1.3).
Nicolas Carnot, Vincent Koen, Bruno Tissot

2. The Data

Abstract
Measuring and interpreting economic developments requires a set of statistical standards as well as a coherent overall framework. By and large, forecasters around the world use a common language and refer to the same key macroeconomic variables. However, there are differences across time and space regarding the specific empirical content of some of the concepts used. For most practical purposes, at least in forecasting, these differences are relatively minor, but they ought to be borne in mind when laying side by side national forecasts or comparing performance across countries.
Nicolas Carnot, Vincent Koen, Bruno Tissot

3. Incoming News and Near-Term Forecasting

Abstract
The first step in economic forecasting is to establish where the economy actually is. This constitutes a prerequisite for a proper forecast of where it is heading (Section 3.1). The starting point is therefore the collection of a great variety of economic information with a view to assessing the state of the economy before national accounts data become available, weeks or months down the road (Section 3.2). In this regard, survey data can provide precious insights, almost in ‘real time’, and usefully complement the ‘hard data’ due to be released later on (Section 3.3). Once reasonably comprehensive information has been gathered, the challenge is to make sense of it, by adjusting the raw data in various ways and combining some of them to build summary indicators (Section 3.4). In this context, bridge models are especially useful for near-term forecasting purposes (Section 3.5). Even so, macroeconomic monitoring involves difficult trade-offs between abundant but often seemingly inconsistent bits of information (Section 3.6).
Nicolas Carnot, Vincent Koen, Bruno Tissot

4. Time Series Methods

Abstract
This chapter discusses how times series methods can be used for forecasting purposes. As mentioned in Chapter 1, their main distinguishing feature is that they take a statistical view which leaves limited room for economic analysis. Their appeal stems from the ease with which they allow generation of numerical forecasts for a host of variables. The flipside is that these forecasts do not lend themselves to much if any economic interpretation, which is a major handicap when it comes to disseminating and explaining them. Therefore, times series methods usually play an ancillary role, as an auxiliary tool or a benchmark. They can be useful to produce forecasts that are needed but for which the available resources are limited, such as ad hoc extrapolations in the context of business cycle analysis or forecasts of exogenous variables in a macroeconomic model. They can also serve as a check on forecasts obtained through other methods.
Nicolas Carnot, Vincent Koen, Bruno Tissot

5. Modelling Behaviour

Abstract
This chapter presents the specifications that are generally adopted to describe agents’ economic behaviour. Section 5.1 deals with enterprise investment (fixed and stockbuilding), which plays a prominent role as a driver of economic fluctuations. Section 5.2 proceeds with household spending (consumption and residential investment). Section 5.3 discusses imports, exports, world demand and competitiveness. Section 5.4 covers employment and Section 5.5 wage-price dynamics. In the process, this chapter surveys the commonly used specifications, including their theoretical underpinnings and their typical results. The presentation draws heavily on error-correction equations, as they are intuitive, enlightening and frequently used by practitioners (see Box 5.1). Estimation methods per se are beyond the scope of this book but are presented in standard econometrics textbooks.
Nicolas Carnot, Vincent Koen, Bruno Tissot

6. Macroeconomic Models

Abstract
A macroeconomic (or macroeconometric) model is a quantitative representation of an economy, or of several interdependent countries. It assembles a number of equations and allows study of the behaviour of the economy(ies) when all the various relationships between variables are operative simultaneously. In addition, a model synthesises data and knowledge with a view to explain economic history better and to forecast future developments.
Nicolas Carnot, Vincent Koen, Bruno Tissot

7. Medium- and Long-Run Projections

Abstract
Interest in medium- and long-run projections is on the rise, not least due to concerns about the economic implications of population ageing, but also against the background of the ongoing rebalancing of the global economy towards some large emerging economies, especially in Asia. Medium- and long-run projections differ from the short-run forecasts discussed in previous chapters. Beyond the next few quarters, it is indeed pointless to try and forecast the business cycle. However, it is possible and instructive to look at the underlying trends further out. To differentiate the two types of exercises, the term ‘projection’ tends to replace that of ‘forecasts’ for longer-run horizons. Even so, projections are and should be quantified rather than purely qualitative. In this context, the numbers are not meant to convey what would be an illusory sense of precision. Rather, they serve to illustrate broad trends and to highlight constraints, notably through recourse to alternative scenarios.
Nicolas Carnot, Vincent Koen, Bruno Tissot

8. Financial and Commodity Markets

Abstract
Financial and commodity prices are not forecasted in the same way as national accounts aggregates. These prices are volatile and very much driven by market participants’ expectations. A great variety of tools are used to analyse and forecast them, ranging from traditional models of interest rate determination to ‘heterodox’ methods such as chartism, to country-risk analysis.
Nicolas Carnot, Vincent Koen, Bruno Tissot

9. Budget Forecasts

Abstract
Budget forecasts try to anticipate the evolution of the fiscal accounts based on an economic scenario. They are therefore one specific aspect of overall economic forecasting. They range from the rather global to the very detailed, depending on the purpose of the exercise (Section 9.1). Over the short and medium run, budget forecasting mainly involves assessing the sensitivity of receipts and spending to changes in macroeconomic conditions and estimating the impact of new measures (Section 9.2). Over the long run, the focus is more on the evolution of public spending and on the underlying demographic trends (Section 9.3). Budget scenarios can be put together in several ways (Section 9.4), following a fairly general top-down approach or a more detailed bottom-up one (Section 9.5). In practice, these two approaches complement each other. Budget forecasts come with substantial error margins, which is a cause for concern given the large size of the public sector in the overall economy (Section 9.6). One way to analyse and prevent fiscal surprises is to look at the budget from a more analytical angle, bringing in the concepts of structural balance, debt sustainability and rules (Section 9.7).
Nicolas Carnot, Vincent Koen, Bruno Tissot

10. Sectoral Forecasting

Abstract
This book mostly deals with forecasting developments at the macroeconomic level, even when discussing specific aspects such as financial markets (Chapter 8) or fiscal policy (Chapter 9). In contrast, sectoral forecasts follow a microeconomic approach. Here, the focus is on sectors, or even on one particular sector. This is a relevant perspective for a firm concerned with the outlook its sector faces, or for a local government wondering how much needs to be spent on infrastructure to ensure that firms in the area can grow without encountering local bottlenecks (such as road congestion).
Nicolas Carnot, Vincent Koen, Bruno Tissot

11. Accuracy

Abstract
Though demand for economic forecasts is strong, their reliability is frequently questioned and scepticism is widespread among the general public regarding the accuracy of any forecast. Granted, forecasters often get it wrong, not least when it comes to foreseeing turning points or crises. But uncertainty is part and parcel of advanced economies. In addition, forecasters themselves consider that they tend to be unduly criticised. They argue that their track record is more respectable than is often alleged, noting that professional forecasts have been proved to be more reliant than the so-called ‘naïve forecasts’ produced by elementary methods. Moreover, accuracy is perhaps not the best criterion to judge the value of forecasts. Indeed, their usefulness may have more to do with the associated diagnoses and policy implications (see Chapter 12).
Nicolas Carnot, Vincent Koen, Bruno Tissot

12. Using the Forecasts

Abstract
Demand for forecasts obviously arises from a need to form a view on the future.1 A government, for example, when preparing next year’s budget, has to rely on a forecast of activity in order to quantify the foreseeable tax receipts. Social partners have to refer to some forecast of inflation when negotiating wage increases. Firms contemplating investment in new factories try to anticipate demand for the corresponding output. In fact, in virtually all walks of economic life, agents regularly use forecasts as inputs into their decisions. That said, forecast accuracy is far from perfect, as discussed in Chapter 11, and preferences and constraints along with forecasts matter in framing decisions.
Nicolas Carnot, Vincent Koen, Bruno Tissot

13. Communication Challenges

Abstract
Forecasts need to be explained both to policymakers and to other clients, but also to a wider public. This raises communication issues. First, some of the technical subtleties are difficult to explain in a simple way, not least as regards the uncertainties surrounding the forecast (Section 13.1). Second, there is the question of how much transparency is desirable: to what extent should governments and central banks publish their forecasts or keep them confidential (Section 13.2)? Lastly, in the case of official forecasts, it is important to acknowledge their ambivalent status: they are both a technical and a political exercise, and this raises tensions that need to be addressed (Section 13.3).
Nicolas Carnot, Vincent Koen, Bruno Tissot

14. A Tour of the Forecasting Institutions

Abstract
Myriad public, semi-public and private bodies are engaged in regular or occasional forecasting. Users are also very diverse. On the supply side of the market for forecasts, some producers are driven by profit motives, while others offer a public good. The demand side includes policymakers – especially finance ministers and central bank governors – but also a broader public, say, the readers of the financial press. The product itself is far from homogeneous: many forecasts cover only the short run, but some extend over longer horizons, and quality is very uneven. It is also a market with fads, as some forecasters’ reputation rides high when they surf on a run of successful predictions, but can suddenly collapse when they fail to foresee some major turning point. Lastly, price-setting is rather opaque in this market, since cross-subsidisation is rife, including in the private sector.
Nicolas Carnot, Vincent Koen, Bruno Tissot

Epilogue

Abstract
A British Chancellor of the Exchequer who suffered more than others from the major forecasting errors made under his watch decided to take revenge on the technicians and declared in his Budget statement:
Like long-term weather forecasts, they [economic forecasts] are better than nothing …. but their origin lies in the extrapolation from a partially known past, through an unknown present, to an unknowable future according to theories about the causal relationships between certain economic variables which are hotly disputed by academic economists and may in fact change from country to country or from decade to decade (Healy, 1990).
Nicolas Carnot, Vincent Koen, Bruno Tissot

Backmatter

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