# Preface

My interest in business cycles was rekindled by Professor Jim Ford, my mentor during the first part of my career at the University of Birmingham. Since completing my PhD on business cycles in 1983, my lecturing and research had focused on money, banking and finance. Jim introduced me to Shackle's much neglected work on business cycles, which is discussed in Chapter 4 and emphasizes the key role bank lending decisions play in the propagation of business cycles.

The 2007-9 Global Financial Crisis (GFC) was a clear demonstration of the role of bank lending in the propagation of financial crises and business cycles and a reminder that Minsky's financial stability hypothesis, discussed in Chapter 3, had also been reflected, but remained highly relevant to modern banking systems. Indeed the onset of the GFC has been described as a 'Minsky moment' when the euphoria of the credit and house price bubbles in the US and elsewhere, turned to 'revulsion' and panic, resulting in a major recession.

This second edition revisits the topic of the role of the banking system in generating financial crises and business cycles in the light of the biggest financial crisis since the 1930s.

Andy Mullineux Professor of Global Finance Birmingham Business School University of Birmingham, UK.

# The Nature of the Business Cycle

## Definitions

Perhaps the most widely quoted and influential definition is that of Burns and Mitchell (1946, p.l.) who state that:

Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organise their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; the sequence of changes is recurrent but not periodic; in duration cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar character with amplitudes approximating their own.

A number of features of this definition should be highlighted. Firstly, it stresses only two phases of the cycle, the expansionary and contractionary phases. It will be seen in section 1.2 that the peak or upper turning point and the trough or lower turning point are not analyzed as distinct phases but are merely used to identify business cycles in aggregate economic time series. Many economists, however, regard the turning points as particular phases requiring separate explanation. This is especially evident in the discussion of the financial instability hypothesis, which stresses the role of financial crises in terminating the boom phase, in Chapter 3.

The second main feature is the emphasis on the recurrent nature of the business cycle, rather than strict periodicity. Combined with the wide range of acceptable durations, encompassing both major and minor cycles (Hansen 1951), this means that cycles vary considerably in both duration and amplitude and that the phases are also likely to vary in length and intensity. Minor cycles are often assumed to be the result of inventory cycles (Metzler 1941), but Burns and Mitchell reject these as separable events as postulated by Schumpeter (1939), among others.2 Finally, and perhaps most importantly, they emphasise comovements as evidenced by the clustering of peaks and troughs in many economic series. This is a feature stressed in numerous subsequent business cycle definitions, a sample of which are discussed below.

The original National Bureau of Economic Research (NBER) work of Burns and Mitchell concentrated on the analysis of non-detrended data. In the post-war period such analysis has continued but the NBER has also analysed detrended data in order to identify growth cycles, which tend to be more symmetric than the cycles identified in non-detrended data. The issue of asymmetry is an important one because it has implications for business cycle modeling procedures; it will be discussed further in section 1.3.

Concerning the existence of the business cycle, there remain bodies of atheists and agnostics. Fisher (1925, p. 191) is often quoted by doubters and disbelievers. He states:

I see no reason to believe in the Business Cycle. It is simply a fluctuation about its own mean. And yet the cycle idea is supposed to have more content than mere variability. It implies a regular succession of similar fluctuations constituting some sort of recurrence, so that, as in the case of the phases of the moon, the tides of the sea, wave motion or pendulum swing we can forecast the future on the basis of a pattern worked out from past experience, and which we have reason to believe will be copied in the future.

The work done at the NBER has subsequently attempted to show that there is indeed more to the business cycle than mere variability. Doubters remain, however, and tests of Fisher's so-called Monte Carlo hypothesis will be discussed in section 1.2.

The NBER view that there is sufficient regularity, particularly in comovements, to make the business cycle concept useful is shared by two of the most distinguished students of cycle theory literature, Haberler (1958, pp. 454-9) and Hansen. Hansen (1951) notes that some would prefer to substitute 'fluctuations' for cycles but concludes that the usage of the term cycles in other sciences does not imply strict regularity. This point is also made by Zarnowitz and Moore (1986) in a recent review of the NBER methodology.

Lucas (1975) helped to rekindle interest in business cycle theory4 by reviving the idea of an equilibrium business cycle. The cycle had tended to be regarded as a disequilibrium phenomenon in the predominantly Keynesian contributions to the post-war cycle literature. Lucas (1977) discussed the cycle in more general terms and stressed the international generality of the business cycle phenomenon in decentralised market economies. He concluded (p. 10) that: with respect to the qualitative behaviour of comovements among series, **business cycles are all alike.**

And that this:

suggests the possibility of a unified explanation of business cycles, grounded in the * general *laws governing market economies, rather than in political or institutional characteristics specific to particular countries or periods.

The intention here is not to deny that political or institutional characteristics can influence actual cycle realisations and help account for their variation between countries and periods. It is rather to stress the existence of general laws that ensure that a market economy subjected to shocks will evolve cyclically. Research that aims to gauge the extent to which the US business cycle has changed since the Second World War is reviewed in section 1.5.

Sargent (1979, p. 254) attempts to formalize a definition of the business cycle using time series analysis. He first analyses individual aggregate economic time series and arrives at two definitions. Firstly:

A variable possesses a cycle of a given frequency if its covariogram displays damped oscillations of that frequency, which is equivalent with the condition that the non-stochastic part of the difference equation has a pair of complex roots with argument... equal to the frequency in question. A single series is said to contain a * business cycle *if the cycle in question has periodicity of from about two to four years (NBER minor cycles) or about eight years (NBER major cycles).

Secondly, Sargent argues that a cycle in a single series is marked by the occurrence of a peak in the spectral density of that series. Although not equivalent to the first definition, Sargent (1979, Ch. XI) shows that it usually leads to a definition of the cycle close to the first one.

Sargent (1979, p. 254) concludes that neither of these definitions captures the concept of the business cycle properly. Most aggregate economic time series actually have spectral densities that display no pronounced peaks in the range of frequencies associated with the business cycle, and the peaks that do occur tend not to be pronounced. The dominant or 'typical' spectral shape - as dubbed by Granger (1966) -of most economic time series is that of a spectrum which decreases rapidly as frequency increases, with most of the power in the low frequency, high periodicity bands. This is characteristic of series dominated by high, positive, low order serial correlation, and is probably symptomatic of seasonal influences on the quarterly data commonly used. Sargent warns, however, that the absence of spectral peaks in business cycle frequencies does not imply that the series experienced no fluctuations associated with business cycles. He provides an example of a series which displays no peaks and yet appears to move in sympathy with general business conditions. In the light of this observation Sargent (1979, p. 256) offers the following, preferred, definition, which emphasises comovements:

The business cycle is the phenomenon of a number of important economic aggregates (such as GNP, unemployment and lay offs) being characterised by high pair wise coherences6 at the low business cycle frequencies, the same frequencies at which most aggregates have most of their spectral power if they have 'typical spectral shapes'.

This definition captures the main qualitative feature or 'stylised fact' to be explained by the cycle theories discussed in Chapter 2.

The dominant methodology of business cycle analysis is based on the Frisch-Slutsky hypothesis discussed in section 1.4. Low order linear deterministic difference or differential equation models cannot yield the irregular non-damped or non-explosive cycles typically identified by the NBER, but low order linear stochastic models can yield a better approximation,7 as Frisch (1933) and Slutsky (1937) observed. Sargent (1979, pp. 218-19) observes that high order non-stochastic difference equations can, however, generate data that looks as irregular as typical aggregate economic time series. By increasing the order of the equation, any sample of data can be modeled arbitrarily well with a linear non-stochastic differential equation. This approach is generally not adopted, however, because the order usually has to be so high that the model is not parsimonious in its parameterisation (Box and Jenkins, 1970) and there will be insufficient degrees of freedom to allow efficient estimation. Further, it allocates no influence at all to shocks. An alternative to high order linear models that can also produce an essentially endogenous cycle, in the sense that the shocks merely add irregularity to a cycle that would exist in their absence, is to use nonlinear models which can have stable limit cycle solutions (see section 2.3). While it is generally accepted that stochastic models should be used, because economies are subjected to shocks, there is no general agreement over the relative importance of the shock-generating process and the economic propagation model in explaining the cycle, or on whether linear or nonlinear models should be used. The dominant view, however, appears to be that linear propagation models with heavy dampening are probably correct and that we should look to shocks as the driving force of the (essentially exogenous) cycle. Blatt (1978), however, showed that the choice of a linear model, when a nonlinear one is appropriate, will bias the empirical analysis in favour of the importance of shocks. It is in the light of this finding that the empirical results discussed in the following chapters, which are invariably based on econometric and statistical techniques that assume linearity, should be viewed.

A related issue is the tendency to regard the business cycle as a deviation from a linear trend.8 Burns and Mitchell (1946) expressed concern about such a perspective and analysed non-detrended data as a consequence. In the post-war period, however, even the NBER has begun to analyse detrended data in order to identify growth cycles, although the trend used is not linear.9 Nelson and Plosser (1982) warn of the danger of this approach, pointing out that much of the so-called cyclical variation in detrended data could be due to stochastic variation in the trend which has not in fact been removed. If the trend itself is nonlinear, linear detrending is likely to exaggerate the cyclical variation to be explained and introduce measurement errors. This and related issues will be discussed further in sections 1.2 and 4.3.2.

Despite the voluminous empirical work of the NBER and the work of other economists, a number of questions remain unresolved. Firstly, are there long cycles and/or nonlinear trends? This question will be considered further in section 4.3. It is of crucial importance because the analysis of the business cycle requires that it must somehow first be separated from trend and seasonal influences on the time series.10 The appropriate method of decomposition will not be the subtraction of a (log) linear trend from the deseasonalised series if the trend is not (log) linear. Secondly, to what extent is the cycle endogenously and exogenously generated? Most business cycle research assumes that linear models can be used to describe an economic system which is subjected to shocks. The stochastic linear models employed can replicate observed macroeconomic time series reasonably well because the time series they produce possess the right degree of irregularity in period and amplitude to conform with actual realisations. Such models are based on the Frisch-Slutsky hypothesis, discussed in section 1.4. The hypothesis assumes that linear models are sufficient to model economic relationships. Because the estimated linear econometric models display heavy dampening, cycle analysts have increasingly turned their attention to trying to identify the sources of the shocks that offset this dampening and produce a cycle. Chapter 2 reviews some recent work on the sources of shocks which drive cycles in the US economy. The current trend is, therefore, towards viewing the cycle as being driven by exogenous shocks rather than as an endogenous feature of the economy. However, nonlinear mathematical business cycle modeling provides the possibility that stable limit cycles, which are truly endogenous, might exist; recent literature on such models is reviewed in section 2.3."

Mullineux (1984) discusses the work of Lucas (1975, 1977), who stimulated renewed interest in the equilibrium theory of the business cycle. Lucas's cycle was driven by monetary shocks but subsequent work has emphasised real shocks; consequently, there has been a resurgence of the old debate over whether cycles are real or monetary in origin. Section 2.2 reviews the theoretical contributions to the debate, section 1.5 looks at work attempting to identify the main sources of shocks, and in Chapter 3 it is argued that monetary and financial factors are likely to play at least some role, alongside real factors, in cycle generation.

In the next section, the question of the business cycle's very existence will be considered, while in section 1.3 the question of whether or not cycles are symmetric, which has a bearing on the appropriateness of the linearity assumption, will be explored.