a multidimensional framework for measuring business...

A Multidimensional Framework for

Measuring Business Cycles

Anirvan Banerji a and Lorene Hiris b

a Director of Research, Economic Cycle Research Institute

420 Lexington Avenue, Suite 1645,New York, NY 10170Tel: (212) 557-7788 Fax: (212) 557-9874

b Professor of Finance, C. W. Post/Long Island University and

Senior Research Scholar, Economic Cycle Research Institute720 Northern Blvd., Brookville, NY 11548

Tel: (516) 299-2308 Fax: (516) 299-3925

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AbstractThe classical measurement of business cycles, growth cycles, and growth rate cycles lies at

the foundation for the understanding of macroeconomic dynamics in open market economies. Thisessay presents a framework for analyzing and forecasting cyclical behavior in economic activity,employment, and inflation. The framework is extended to foreign trade and important domesticsectors of an economy such as manufacturing, services, and construction. This multidimensionalframework, which allows for a more in-depth analysis, serves as a model to be developed on acomparable basis across countries. Business cycle and growth rate cycle reference chronologies,which have been determined for the major economies, are presented in this context.

Keywords: Business Cycles, Growth Rate Cycles, International Reference Cycles, International Reference Chronologies,International Cycle Dates, Turning Points, Cyclical Analysis, Forecasting, Leading Indexes, Long Leading Indexes, ShortLeading Indexes, Economic Sectors, Inflation Cycles, Employment Cycles, Foreign Trade, Exports, Imports.

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1. Introduction

While the index of leading economic indicators (LEI) is still popularly perceived as the chief

forecasting tool available for predicting U.S. recessions and recoveries, it is not widely known that

the classical National Bureau of Economic Research (NBER) approach may be applied within a

multidimensional framework for a much more fine-grained, nuanced analysis of economic cycles. In

fact, in the decades since the LEI’s creation, the general approach has been refined under the

guidance of its creator, Geoffrey H. Moore, and applied in a consistent manner to a variety of

economies.

One important finding is that leading indicators of business cycles, when used in the form of

growth rates, also lead cyclical turns in the growth rates of coincident indicators. In fact, it is useful

to perform complementary cyclical analyses in terms of growth rate cycles. In order to identify

these business cycles and growth rate cycles, we first present the defining characteristics of

economic cycles, including cyclical co-movements and stable sequences of leads and lags in cyclical

indicators.

Next, a multidimensional framework that permits more in-depth monitoring through a closer

look at three key aspects of the economy — aggregate economic activity (the traditional domain of

the LEI), inflation, and employment – is described. Aggregate economic activity may be divided

into foreign trade and domestic economic activity. The latter, in turn, can be subdivided into the

major sectors of the economy – services, manufacturing and construction. Each of these areas

exhibits distinct economic cycles, marked by the co-movement of many economic activities within

each area. Also, within each of these areas, specialized coincident and leading indexes can be

designed to anticipate the distinct cyclical movements. While this framework has been fully fleshed

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out for the U.S. economy, with composite indexes for each aspect and sector currently available,

the framework is still in the process of being fully extended to the other major economies.

The international extension of this framework with respect to economic activity is then

presented, and comparable business cycle and growth rate cycle reference dates for the U.S. and

other major economies are defined. Comparable sets of coincident and long leading indexes for

each of these major market economies are also presented.

In sum, this paper shows how the classical indicator approach to forecasting can be refined and

applied in a consistent fashion to many economies within a multidimensional framework, allowing for

more in-depth analysis as well as greater breadth of application.

2. Business cycles and growth rate cycles

Leading indicators were originally designed to anticipate traditional cyclical downturns and

upturns in economic activity (i.e., recessions and recoveries). By the end of the 1960s, however,

many industrial economies had not experienced a recession for many years. This led some

observers to question whether the business cycle was still in existence (Bronfenbrenner, 1969).

Subsequently, there was a move among students of business cycles to study the growth cycle,

which based cyclical analysis on the deviations in economic activity from trend (Mintz, 1969). A

few years later when the OECD developed leading indicators for its member countries, it decided to

monitor these growth cycles. Growth cycle analysis also formed the basis for the international

economic indicators (IEI) project (Klein and Moore, 1985) started at the NBER in the early 1970s.

While growth cycles are not hard to identify in a historical time series, they are difficult to

measure accurately on a real-time basis (Boschan and Banerji, 1990). This is because the trend

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over the last couple of years must be estimated, and these trend estimates tend to be very unstable

near the end (Cullity and Banerji, 1996). This difficulty makes growth cycle analysis less than ideal

as a tool for monitoring and forecasting economic cycles in real time, even though it still useful for

the purpose of historical analysis.

By the late 1980s, the use of growth rate cycles for the measurement of series, which

manifested few actual cyclical declines but did show cyclical slowdowns, was introduced (Layton

and Moore, 1989). Like the “step cycle” introduced by Mintz (1969), the growth rate cycle was

based on the growth rate of economic activity. However, unlike the step cycle, it did not presume

that the growth rate changed in steps. The growth rate cycle was based on the "six month

smoothed growth rate" concept, which avoids the sort of extrapolation of the past trend needed in

growth cycle analysis. This smoothed growth rate is based on the ratio of the latest monthly figure

to the average of the preceding twelve months. Cyclical turns in this growth rate define the growth

rate cycle (Banerji, 1999). Turning points in the growth rate cycle could be identified by the same

objective procedures (Bry and Boschan, 1971) used to identify business cycle turning points.

The growth rate cycle not only avoids the problems of trend estimation presented by the growth

cycle in the case of real-time monitoring, but also shares key cyclical characteristics exhibited by the

business cycle. Most importantly, the cyclical turns in the broad measures of aggregate economic

activity in the form of output, income, employment, and sales cluster together, whether viewed in the

framework of business cycles or growth rate cycles. Moreover, when growth rates are used, the

analysis still generates stable sequences of leading indicators that anticipate business cycles.

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What has emerged in recent years is the recognition that business cycles, growth cycles and

growth rate cycles can all be monitored in a complementary fashion. However, of the three,

business cycles and growth rate cycles are more suitable for real-time monitoring and forecasting,

while growth cycles are more suitable for historical analysis (Klein, 1998).

2.1. Pronounced, pervasive and persistent swings in levels and growth rates

The identification of an economic fluctuation as a cyclical movement is based essentially on the

“three P’s,” i.e., whether the movements are pronounced, pervasive and persistent. The concept of

the three P’s is not a new idea as it is inherent in the notion of the three D’s (duration, depth and

diffusion), which are established criteria for measuring the severity of a recession (Fabricant, 1972).

However, while the three D's apply only to cyclical downturns, the three P’s apply to both upturns

and downturns.

A fundamental feature of a free market economy is the cyclical diffusion or pervasiveness of

movement of indicators of economic activity. During a cyclical upswing, the improvement in

economic activity spreads from one firm to another, from one industry to another, from one region

to another. Moreover, these spreading movements snowball over time, as an expansion or

contraction unfolds.

Another important characteristic of a cyclical upswing or downswing is its persistence. A

move in cyclical indicators that is pronounced and pervasive, but lasts only two to three months

does not qualify as a cyclical movement. Technically, a cyclical upswing or downswing has to

persist at least five months (Bry and Boschan, 1971), but most last much longer.

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Finally, cyclical changes tend to be pronounced in magnitude, compared with non-cyclical

fluctuations, or noise. In particular, the magnitude of the upswings or downswings must be

comparable to those exhibited in previous cyclical episodes.

Of the three P’s, pervasiveness, or the co-movement of many indicators, is necessarily one

that can be defined only with respect to many series considered together. The other two P’s can be

considered, however, for individual time series, for which cyclical turns specific to the series can be

identified.

2.2. The clustering of cyclical turning points: evidence of co-movements

The robustness of the classical approach to cyclical analysis is based on the clustering of

cyclical turns in the indicators, as well as the ability of leading indicators to anticipate cyclical turns in

the economy. The objective identification of such cyclical turning points is critical to cyclical

analysis. Since leading indicators are meant primarily to forecast business cycle turning points, the

identification of turning points in time series is a sine qua non for appropriate evaluation of

forecasting performance. In order to identify cyclical upswings and downswings, and the turning

points that demarcate them, an algorithm for the identification of turning points, based on a

systematic codification of the judgmental procedures, is used. Such a procedure was devised

almost three decades ago (Bry and Boschan, 1971) shortly after the creation of the LEI, and was

used for decades at the NBER.

The objective, though not mathematically simple, definition of turning points given by Bry and

Boschan’s algorithmic formulation of the classical NBER procedure makes it possible to evaluate

the performance of indicators in terms of timing of cyclical turns. The computerized Bry-Boschan

algorithm was used extensively in the years following its creation (e.g., Klein and Moore, 1985).

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Other users of this algorithm have included King and Plosser (1989), who provide a description of

the procedure. As Watson (1994) has pointed out, the Bry-Boschan procedure provides a good

way to define turning points since it is based on objective criteria for determining cyclical peaks and

troughs.

The output from the Bry-Boschan program is used as the basis for the determination of

reference chronologies as well. In the NBER tradition, the turning points are determined for all the

major measures of aggregate economic activity, with specific reference to output, income,

employment and sales. The turning points are then “clustered,” i.e., the reference cycle turning point

is chosen on the basis of the best consensus among the turning point dates for these individual

indicators. The business cycle and growth rate cycle dates identified by such an objective

procedure are characterized by the three P’s — pronounced, pervasive, and persistent movements

in the cyclical indicators between turning points.

This “clustering” of turning points reflects the cyclical co-movement of many economic activities

that is the hallmark of an economic cycle. It is in the context of such “clustering” of cyclical turns

that the differences in the three aspects and various sectors of an economy can be fully appreciated.

One key reason for distinguishing among cycles in aggregate economic activity, inflation and

employment is that while cyclical turns cluster quite clearly within each of these aspects, the resulting

cycles are different from each other. Similarly there is strong evidence of clustering of turns within

services, manufacturing, and construction, and the resultant cycles are quite distinct.

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3. The multidimensional framework

The multidimensional framework for the analysis of economic cycles applies to both the levels of

economic activity and their growth rates. However, the latter are particularly relevant for the service

sector and many international economies, which may exhibit relatively rapid growth without absolute

declines. While the focus in the 1960s and 1970s remained on cycles in economic activity or its

growth rate, by the early 1980s it had become clear that cyclical activity was not one-dimensional,

and that there were cycles in other important aspects of an economy that were worth monitoring,

specifically, inflation and employment.

Of course, the LEI was used to predict cycles in aggregate economic activity or economic

growth. A key step forward in the evolution of the multidimensional framework was the separation

of leading indicators with long leads from those with short leads (Cullity and Moore, 1990). The

former may be combined into a long leading index, and the latter into a short leading index, while

coincident indicators are combined into a coincident index. Such a set of long leading, short leading

and coincident indexes results in a sequential system for monitoring cyclical developments.

Because inflation exhibits cycles distinct from economic activity, a composite leading index may

be constructed specifically to lead inflation cycles. Since employment cycles differ from business

cycles as well, just as inflation cycles do, a composite leading index may also be constructed

specifically to predict employment cycles. Independent corroboration of the validity of such a

multidimensional cyclical structure in the economy comes from Stock and Watson (1998), who

conducted a large-scale factor analysis of a large number of U.S. time series. They found that

according to the factor loadings, the first factor related to measures of economic activity and in

particular measures of output similar to industrial production. The second (and orthogonal) factor

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had strong factor loadings on money, earnings, and prices, which are of course linked directly to

inflation. The third factor had heavy factor loadings on variables related to employment. This

suggests that the proposed multidimensional cyclical framework covers the three most important

distinct dimensions of the U.S. economy.

The framework was further extended with the recognition that the economic activity aspect of

the economy can be divided into foreign trade and domestic activity. The latter can be subdivided

into three major sectors — services, manufacturing, and construction.

3.1. Inflation and employment

In the aftermath of the stagflation of the late 1970s, it was quite obvious that there could be

important divergences between cyclical behavior in economic growth, in inflation, as well as in

employment. While these variables are loosely related, it became increasingly clear that they could

exhibit very different cyclical timing.

These developments gave a fresh impetus to the development of an index of leading indicators

of inflation (Moore, 1980). These indicators typically reflect the degree of tightness in employment,

financial, or domestic or foreign goods markets, and thus measure underlying inflationary pressures.

Such inflation-specific leading indicators can be combined into a Future Inflation Gauge (FIG).

Similar composite leading inflation indexes have also been developed by other researchers (Niemira

and Klein, 1994). Other researchers have also focused on the inflation cycle, as distinct from the

business cycle (Roth, 1986; Ivanova, Lahiri, and Seitz, 2000).

The distinction between cycles in economic growth and inflation once again came to the fore in

the late 1990s, when the U.S. economy experienced several years of non-inflationary growth.

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During this period, the FIG accurately predicted subdued inflation, even as the leading indicators of

economic growth correctly forecast a robust economy. In this case, overall inflationary pressures,

as measured by the FIG, were kept in check by imported disinflationary pressures, even as

joblessness fell and domestic inflation pressures climbed.

In general, while it is true that cyclical downturns in inflation follow growth slowdowns about 70

percent of the time, growth slowdowns actually follow inflation downturns the other 30 percent of

the time. In other words, economic growth or the unemployment rate alone can be imprecise

predictors of the timing of cyclical turns in the inflation cycle. Hence the need for leading indicators

of inflation distinct from leading indicators of economic activity.

Another important aspect of the economy is employment. In the early 1980s, Moore and his

colleagues developed a specialized leading index to anticipate changes in employment conditions

(Moore, 1981). The Coincident Employment Index is designed to move in step with the cyclical

movement in employment conditions, while the Leading Employment Index is designed to anticipate

these employment cycles. In early 1990, the Leading Employment Index forecast a sharp and

recessionary rise in the jobless rate, and was the key to Geoffrey Moore’s prediction1 of a

1 The importance of the employment indicators to Moore’s prediction is evident from the description inSebastian (1990), from The Wall Street Journal dated March 9: “Geoffrey Moore, who at 75 years of age has had ahand in declaring many modern recessions, gives his opinion even without being asked. Mr. Moore, director ofColumbia University's Center for International Business Cycle Research, recently noted the center's employmentindex has begun signaling recession ... Newly pessimistic, Mr. Moore puts the odds of a recession at two-to-onein the first half. "If we escape recession in the first half, I'd change the odds to fifty-fifty in the second half." Mr.Moore admits that the center's long-leading index – its main predictor – is still very strong. "I had somedifficulty reconciling that. Maybe it means a recession will be very brief if it comes." ”

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recession (Sebastian, 1990) that newer econometric methods failed2 to predict. The recession did

start in July 1990, but the government’s LEI did not predict the recession3 either (Wessel, 1990)

until much later in the year.

Although strong employment growth can be a source of inflationary pressure, it is by no means

the only factor that determines future inflation. There are many other factors, such as growth in the

prices of industrial materials and imports, credit growth and tightness in supply lines. Much of the

time, the influences of these factors act in concert, and quicker employment growth is accompanied

by faster price rises in commodity markets, rapid expansion of credit and increasing delays in supply

lines. The pervasive influence of all these factors usually leads to an inflation upswing.

However, at certain times the link between employment growth and some of these other

indicators may be broken. This may happen, for instance, because of central bank action or

because of adverse cyclical developments in other countries. In these cases, the inflationary

influence of high employment would be more than counteracted by other disinflationary or even

deflationary influences, resulting in low inflation in spite of tight labor markets.

2 In the same article, Sebastian (1990) wrote: “The just-released January reading on the NBER's new recessionindex puts the chance of recession at a measly 3% in the next six months … "Our new index would be consistentwith the fourth quarter of 1989 being the worst of quarters" in this cycle, says James Stock, … one of the newindex's creators.”

3 Wessel (1990), writing in The Wall Street Journal dated December 3, noted, “The government's economicforecasting gauge is finally flashing the recession sign, but the warning comes after what most economists saywas the beginning of the downturn … A rule of thumb developed by University of Michigan forecaster SaulHymans is that three consecutive declines in the index signal a recession. The index dropped in August andSeptember, but it wasn't until Friday that the Commerce Department revised the figures for July to show a declinefor that month, too.”

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The link between cyclical swings in employment growth and inflation during the postwar era has

broken down several times. During the stagflation of the late 1970s, employment declined in 1976

and again in 1978, while inflation continued to climb. Also, there were several episodes when

employment grew strongly without causing inflation. Out of 13 upswings in employment during the

postwar period, ten were followed by an inflation upswing within a year or so. In two cases, the

inflation upswing started even before the upswing in employment growth began. However, in three

cases, in 1980, in 1991, and most recently in 1996, a sustained upswing in employment was

accompanied by a sustained inflation downturn. The evidence is clear, therefore, that employment

cycles and inflation cycles are quite distinct from each other. For that reason, in the case of inflation

as well as the case of employment, the notion was that, by combining indicators pertaining to a

single critical macro-economic dimension of economic performance, results would be more precise

and, hopefully, produce longer and more reliable leads for forecasting (Klein, 1993).

It is interesting to note that U.S. Federal Reserve policy during the Greenspan years has been

remarkably well correlated with cycles in the FIG (Banerji and Klein, 2000). According to Coons

(2000), the FIG, which is a measure of inflationary pressures, has “reliably predicted changes in the

direction of the federal funds rate target at least since Greenspan was named Fed Chairman in

1987.” Coons further observes that “no indicator or forecasting method is without its flaws, but this

combination (the FIG) seems to distill from the relevant information available at any time what is

knowable about the future direction of interest rates.”

Thus, the leading indexes of employment and inflation have chalked up impressive achievements

in the past decade. Five months before the 1990-91 U.S. recession began, the employment index

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predicted a sharp increase in unemployment consistent with a recession, which better-known

forecasting tools were unable to foresee. The inflation indicators, on the other hand, accurately

anticipated every directional change in a successful Federal Reserve policy during a period when

many observers invoked a “new paradigm” to explain the apparent delinking of U.S. growth and

inflation.

3.2. Economic activity and growth

Composite indexes of leading, coincident, and lagging indicators have long been constructed for

the purpose of monitoring cycles in economic activity and growth. However, economic activity, one

of the three aspects of the economy, can be broken down into several parts, representing the major

sectors of the economy.

3.2.1. Foreign trade

The traditional leading indicator approach evolved mainly in the context of closed economies.

Yet, with the increasing globalization of the economy, it has become more important to be able to

anticipate the cycles in foreign trade that can significantly impact the domestic economy.

The economic growth aspect of the economy can be split up into two major divisions, one

relating to the domestic economy, and the other to foreign trade. In order to monitor trade flows,

the Leading Exports Index (LExI), based on exchange rates and the long leading indexes for U.S.

trading partners, was developed so that the LExI’s growth rate could anticipate cycles in U.S.

exports growth. Similarly, the growth rate of the Leading Imports Index anticipates cycles in U.S.

imports growth, and the Leading Trade Balance Index anticipates cycles in the U.S. trade balance

(Hiris and Guha, 2000).

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3.2.2. Major sectors of the domestic economy

Traditional leading indexes are designed to anticipate cycles in overall domestic economic

activity. However, the major sectors of the domestic economy do not always move in the same

direction, and it can be of value to subdivide the domestic economy into its three sectors –

manufacturing, services and construction – corresponding to the broad division of gross domestic

product into goods, services and structures.

The manufacturing sector is known to be highly cyclical, exhibiting its own expansions and

contractions in economic activity. Cycles in the manufacturing sector are tracked by the Coincident

Manufacturing Index, which is made up of coincident indicators of manufacturing activity. The

Leading Manufacturing Index, made up of leading indicators specific to manufacturing activity,

anticipates these cycles.

In recent decades, the service sector has become the increasingly dominant part of the U.S.

economy, accounting for more than half of output and four-fifths of employment. Although the

service sector does not typically exhibit the familiar cycles of expansion and contraction, it does

show cyclical speedups and slowdowns in growth. The application of the concept of growth rate

cycles to analyze the service sector of the economy is, therefore, appropriate (Layton and Moore,

1989). The growth rate of the Coincident Services Index, made up of coincident indicators of

services activity, monitors the current state of the service sector’s growth rate cycle, while the

growth rate of the Leading Services Index anticipates those cycles.

The third slice of the domestic economy is the highly cyclical construction sector. The

Coincident Construction Index and Leading Construction Index track these cycles in an analogous

fashion.

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Each of these sectors of the domestic economy, as well as foreign trade, along with the two

other key aspects of the economy, i.e., inflation and employment, exhibit distinct characteristics.

Specifically, they each exhibit pronounced, pervasive, and persistent movements in terms of levels,

growth rates, or both.

3.2.3. Evidence of cyclical behavior: stable sequences of leads

Leading indicators, whose cyclical turns consistently anticipate cycles in the coincident

indicators, provide further evidence supporting the existence of the patterns of cyclical movements.

This is true whether the indicators are viewed in terms of levels or growth rates, and whether it is

overall economic growth or aspects or sectors of the economy that are examined. At the Economic

Cycle Research Institute (ECRI), founded by Geoffrey H. Moore, composite leading and coincident

indexes were created for all of these aspects and sectors of the U.S. economy.

As Table 1 shows, the ECRI leading indexes for each aspect of the U.S. economy as well as

for each major sector consistently lead the corresponding coincident indicators at cyclical turning

points (by convention, leads have negative signs and lags have positive signs). On average, the

Long Leading and Short Leading indexes lead business cycle turning points by nearly a year at

peaks and half a year or less at troughs. A similar asymmetry of leads is seen in the performance of

the Leading Employment Index. The Leading Manufacturing and Construction Indexes have

average leads of about half a year. The Future Inflation Gauge and Leading Trade Balance Index

have leads that are roughly symmetric at peaks and troughs, and close to a year.

As Table 2 shows, the leading character of these indexes is retained when they are examined in

terms of growth rates. However, the leads are now generally shorter and more symmetric. In fact,

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all the above-mentioned indexes lead by about half a year, on average, at both peaks and troughs.

The growth rates of the Leading Services Index and the leading indexes of exports and imports,

which do not exhibit cycles in level terms (Table 1), do show clear cycles in growth rate terms, and

lead by about half a year at both peaks and troughs.

It should be noted that the earliest warning of a cyclical turn is usually obtained from a long

leading index (Cullity and Moore, 1990). In this regard, they are superior to traditional leading

indexes. In practice, because of the normal data publication lags and the inevitable delay in

recognizing a turning point after it occurs, the effective leads of most traditional leading indexes are

often whittled down to virtually zero. Because of their longer lead times, long leading indexes are

less susceptible to this problem, and are therefore more useful to policy makers and others who

need to take early action before a cyclical turn. Such long leading indexes are now available for

over a dozen countries other than the United States.

4. The multidimensional framework in an international context

Wesley Mitchell long ago recognized the need for the study of business cycles in the

international context. More than seventy years ago, he wrote, “For theoretical uses, there is needed

a systematic record of cyclical alternations of prosperity and depression, covering all countries in

which the phenomena have appeared, and designed to make clear the recurrent features of the

fluctuations” (Mitchell, 1927). Three quarters of a century later, Basu and Taylor confirmed, “a

robust and useful theory of business cycles should be able to account for the pattern seen in the

long-run data for many countries” (Basu and Taylor, 1999).

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Some of the most important substantive advances in measuring business cycles have been in the

extension of the classical indicator analysis approach to a number of economies. More specifically,

a major achievement has been the finding that many of the same indicators lead economic cycles in a

diverse collection of economies with different structures. This enables the design of robust systems

of indicators, which can work in a variety of economies and in the same economy through major

structural changes.

Leading and coincident indexes, similar to those developed for the United States, were initially

constructed for other major market economies by Klein and Moore (1985). A further important

enhancement of these efforts ongoing at ECRI is the development of a system of cyclical indicators

which are comparable across borders. Moore and his associates followed a rather strict procedure

in that they assembled data from the other countries on the same types of economic processes that

have proved to be leading indicators in the U.S. In a sense, each country for which these systems

of indicators were constructed served as an out-of-sample test for the selected set of leading

indicators. Such a procedure does not necessarily identify the best leading indicators for each

country over a given time span. What it does yield, however, is a robust set of leading indicators.

There is the tendency for virtually all economists who work on leading indicators to focus on

data from a single country and a limited time period. Unfortunately, business cycles follow many

different patterns, and cycles may arise out of the combined contribution of a number of economic

processes. The contributions of different economic processes change from turning point to turning

point. Also, it is impossible to predict which of these cyclical processes will trigger the next turn.

Thus, single-country data, especially over a period of one or two decades (perhaps three or four

cycles), is simply not enough to permit the coverage of the diversity of these processes.

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What is preferable to such single-country data, but not always available to most researchers, is

the data on cycles in a large variety of free-market economies. While the structural differences can

obviously be an issue, the commonalties can be very revealing in guiding the choice of a robust set

of leading indicators.

4.1 Comparable cyclical indicators across borders

The classical approach represented by Moore and his colleagues has never been to start with a

large list of potential leading indicators, and choose from them just on the basis of the best statistical

fit to a limited amount of single-country data. The guiding principle has always been conceptual in

that the list of indicators that are to constitute a leading index must contain a representative sample

of all the key processes that are known to contribute to the economic cycles being targeted. These

have been based on the detailed cyclical analysis of many free market economies. The choice of

leading indicators is then made from all the available time series that reflect the key processes

mentioned, primarily on the basis of their performance at cyclical turning points. Despite the relative

brevity of the time series, the approach of using roughly equivalent indicators across countries,

instead of choosing the indicators according to the degree of statistical fit, has led to success in

developing economic indexes for countries as diverse as China, Jordan and India. While new

countries are being added to the list on an ongoing basis, it is very important to note that so far, the

selected group of leading indicators has worked successfully in the vast majority of these countries

as it has in the U.S.

The full multidimensional framework developed for the U. S. economy can be successfully

applied, in principle, for other market economies as well. This effort is already underway. In fact,

like the U.S. economy, at least a dozen other market economies exhibit pronounced, pervasive and

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persistent movements in both the levels and growth rates of aggregate economic activity, which are

characteristic of business and growth rate cycles.

As Table 3 shows, the long leading indexes for each of these countries show clear leads at

peaks and troughs of the business cycle. As Table 4 shows, the growth rates of these long leading

indexes also consistently lead the peaks and troughs of their respective growth rate cycles, by about

half a year to a year on average.

4.2 Reference chronologies

It is important to understand the vital importance of a proper set of reference dates for

international business cycles and growth rate cycles. Those reference chronologies really define the

cycles that we seek to compare and contrast across the countries, so it is critical that they should be

selected on the basis of a uniform set of procedures based squarely on the NBER approach long

used in the U.S. The end-result of these efforts is displayed in Tables 5 and 6. These reference

chronologies4 can now serve as benchmarks for use by other researchers who seek to perform

cross-country comparisons of cyclical patterns.

The business cycle peaks and troughs for countries other than the United States were chosen by

applying the same NBER-type “clustering” approach, and these reference cycle dates are shown in

Table 5. The reference cycle dates for the growth rate cycles for the same countries were also

chosen using analogous procedures, and are shown in Table 6. Both sets of reference cycle dates

were determined by the Economic Cycle Research Institute under the guidance of Geoffrey Moore.

4 The latest updates to these reference chronologies are available at www.businesscycle.com.

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What is remarkable about the consistent leads shown in Tables 3 and 4 is that virtually the same

indicators are used for the long range gauges for every country, showing how robust these cyclical

patterns remain in spite of the structural differences among the different economies. The robustness

of these leads also suggests that these same long leading indexes can be used as reliable leading

indicators of economic cycles. It follows, therefore, that these long leading indexes are likely to

remain reliable even after the economy being monitored undergoes significant structural changes.

5. Conclusions

It has been shown that economic cycles can take the form of business cycles or growth rate

cycles. It is important to note that in both cases they are characterized by the pronounced,

pervasive and persistent movements of cyclical coincident indicators. Such movements are the

hallmark of cyclical patterns seen in a wide range of market economies, as well as in critical aspects

of the U.S. economy, and sectors of the U.S. economy. In all of these cases, it is also possible to

design composite leading indexes to anticipate these cyclical turning points — another characteristic

of cyclical processes.

The same set of indicators used to create the U.S. Long Leading Index can be used to put

together long-leading indexes for a variety of market economies. Not only do these economies

exhibit business cycles and growth rate cycles, but these cycles are consistently anticipated by

cycles in the long leading indexes for all those countries.

Thus, what the research has shown is the durability of the phenomena known as business cycles

and growth rate cycles, and the robustness of the long leading indexes that anticipate these cycles in

countries that have differing economic structures.

21

The success of the multidimensional framework for the U.S. strongly suggests the same

approach should be fully extended to other countries. In addition to the coincident and long leading

indexes of economic activity for all the economies, future inflation gauges have already been

constructed for the United Kingdom, Germany, and Japan. Also, a short leading index, as well as

leading indexes of exports, imports and the trade balance have been constructed for the U.K., while

a short leading index and a leading manufacturing index have been developed for Japan.

Over the last few decades, the application of classical approaches to indicator analysis has been

both extensive and intensive, and has yielded robust systems of indicators that have produced

accurate turning point forecasts. An important step has been the development of a multidimensional

framework, which provides much more nuanced and fine-grained insights into the different aspects

of an economy than earlier work on economic indicators. The work of the past few decades,

therefore, has made significant strides in fulfilling Mitchell’s dream of “a systematic record of cyclical

alternations of prosperity and depression, covering all countries in which the phenomena have

appeared, and designed to make clear the recurrent features of the fluctuations” (Mitchell, 1927) to

serve the purpose of serious theory-building.

22

Acknowledgment

The authors gratefully acknowledge the immeasurable debt to their mentor, Geoffrey H. Moore,

whose pioneering work on a great variety of leading indexes inspired the development of the

framework described herein. We further acknowledge the contribution of our colleagues at ECRI

who helped guide the framework’s evolution. In addition, we thank John B. Jones, a referee, and

Kajal Lahiri, an associate editor of this journal, for meaningful and insightful comments.

23

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25

Table 1: United States, 10 Leading Indexes

Timing at Cyclical Peaks and Troughs

Number of Number of Leading IndexesIndex Cyclical Cyclical Average Leads (months) at

Troughs Peaks Troughs Peaks Overall

Long Leading 9 9 -6 -11 -8

Short Leading 9 9 -2 -10 -6

Leading Employment 9 8 -3 -10 -6

Leading Manufacturing 9 9 -3 -7 -5

Leading Services* - - - - -

Leading Construction 9 8 -4 -5 -5

Leading Trade Balance 4 5 -14 -13 -13

Leading Imports* - - - - -

Leading Exports* - - - - -

Future Inflation Gauge 12 11 -12 -11 -12

Source: Economic Cycle Research Institute, New York City

* These indexes do not show clear cycles in terms of levels, but do exhibit growth rate cycles (see Table 2).

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Table 2: United States, 10 Leading Indexes Timing at Growth Rate Cycle Peaks and Troughs

Number of Number ofIndex Growth Rate Cycles Growth Rate Cycles Average Leads (months) at


Long Leading 13 14 -7 -7 -7

Short Leading 14 14 -6 -3 -4

Leading Employment 14 15 -6 -6 -6

Leading Manufacturing 13 14 -5 -6 -5

Leading Services 12 12 -6 -6 -6

Leading Construction 14 13 -5 -8 -6

Leading Trade Balance* - - - - -

Leading Imports 12 13 -7 -8 -7

Leading Exports 5 5 -6 -6 -6

Future Inflation Gauge** - - - - -


* Since the trade balance can be both positive and negative, growth rates of this measure are not appropriate .

** The level of the Future Inflation Gauge is already designed to anticipate cycles in the rate of inflation (see Table 1), i.e., the growth rate of CPI.

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Table 3: Timing at Business Cycle Peaks and Troughs, Long Leading Indexes, 13 Countries

Number of Number of Long Leading IndexesCountry Business Cycle Business Cycle Average Leads (months) at


U.S. 9 9 -6 -11 -8

Canada 2 2 -14 -12 -13

Germany 4 4 -10 -10 -10

France 4 4 -2 -9 -6

U.K. 3 3 -13 -20 -17

Italy 3 2 -11 -12 -11

Switzerland 4 4 -15 -13 -14

Sweden 4 3 -7 -10 -9

Japan 2 3 -12 -10 -11

Korea 2 2 -7 -1 -4

Australia 6 5 -7 -15 -11

Taiwan 1 1 -12 -10 -11

N.Z. 6 6 -5 -4 -4


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Table 4: Timing at Growth Rate Cycle Peaks and Troughs, Long Leading Indexes, 13 Countries

Number of Number of Long Leading IndexesCountry Growth Rate Cycle Growth Rate Cycle Average Leads (months) at


U.S. 13 14 -7 -7 -7

Canada 9 10 -6 -5 -5

Germany 8 7 -12 -10 -11

France 9 8 -4 -7 -6

U.K. 8 8 -5 -11 -8

Italy 4 3 -13 -13 -13

Switzerland 6 7 -12 -8 -10

Sweden 6 6 -4 -9 -6

Japan 11 11 -7 -8 -7

Korea 6 6 -9 -8 -8

Australia 13 13 -7 -6 -6

Taiwan 9 9 -6 -5 -5

N.Z. 7 6 -6 -5 -6


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Table 5: Business Cycle Peak and Trough Dates, 18 Countries, 1948-98 -North America- -Europe- -Asia Pacific-

Peak

or United Canada Mexico Germany France United Italy Spain Switzer- Sweden Japan China* India Korea Australia Taiwan New JordanPeriod Trough States Kingdom land Zealand

1948-1950 P 11/48T 10/49

1951-1952 P 6/51T 8/52 9/52

1953-1955 P 7/53 5/53 12/55T 5/54 6/54 12/54

1956-1959 P 8/57 10/56 11/57T 4/58 2/58 4/59 8/56

1960-1961 P 4/60 12/60T 2/61 9/61

1962-1966 P 3/66 1/64 11/64 6/66T 3/65 11/65

1967-1968 P 4/66T 5/67 4/67 3/68

1969-1973 P 12/69 10/70 10/70 6/72T 11/70 8/71 11/71 5/73

1973-1975 P 11/73 8/73 7/74 9/74 4/74 4/74 7/75 11/73 6/74 12/73 4/74T 3/75 7/75 6/75 8/75 4/75 2/75 1/75 1/75 3/75

1976-1978 P 3/77T 3/76 11/77 3/78

1979-1980 P 1/80 1/80 8/79 6/79 5/80 3/80 2/80 4/79 3/79T 7/80 6/80 3/80 10/80

1981-1983 P 7/81 4/81 3/82 4/82 9/81 6/82 4/82T 11/82 11/82 7/83 10/82 5/81 5/83 11/82 6/83 5/83

1984-1986 P 10/85 11/84T 11/86 12/84 5/84 5/83 3/86

1986-1989 P 9/86 11/87T

1990-1991 P 7/90 3/90 1/91 5/90 11/91 3/90 6/90 3/91 6/90T 3/91 9/91 12/91 6/91 2/91

1992-1994 P 10/92 2/92 2/92 4/92T 3/92 10/93 4/94 8/93 3/92 10/93 12/93 9/93 7/93 2/94

1994-1997 P 11/94 12/94 5/96 8/97 11/95T 7/95 9/96 2/97

1997-1998 P 3/97 10/97T 7/98 5/98

* During the period for which data are available (1984-present), the Chinese economy experienced no business cycle recessions.

SOURCE: For the United States, National Bureau of Economic Research (NBER). For other countries, Economic Cycle Research Institute, New York City NOTE: Shaded cells represent periods for which data are not available.

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Table 6: Growth Rate Cycle Peak and Trough Dates, 18 Countries, 1949-2000 -North America- -Europe- -Asia Pacific-

Peak or United Canada Mexico Germany France United Italy Spain Switzer- Sweden Japan China India Korea Australia Taiwan New JordanPeriod Trough States Kingdom land Zealand

1949-1950 PT 10/49

1950-1952 P 8/50 11/50T 7/52 12/51 8/52

1952-1954 P 3/53 10/52 5/54T 1/54 1/54

1955-1957 P 5/55 8/55 6/56T 11/57 7/56

1957-1958 P 2/57 3/57T 4/58 9/58 4/58 6/58 1/58

1959-1961 P 5/59 4/59 9/60 10/59 2/60 9/60 10/59T 12/60 2/61 5/61 6/61

1961-1963 P 11/61 2/62 2/62 5/62T 12/62 3/63 2/63 3/62 1/63 11/62 5/63

1963-1966 P 1/64 1/65 7/63 7/63 2/64 10/63 5/64 4/64T 11/64 3/65 9/66 3/65 4/65 11/65 1/66 12/65

1966-1968 P 1/66 2/66 7/66 11/67 7/67 4/66 2/67 5/67T 5/67 2/68 3/67 5/68 10/67 3/67 1/68 1/68

1968-1969 P 7/68 1/69 1/69 11/68 3/68 7/69 4/69 6/68 10/68T 9/69 3/69 10/69

1969-1971 P 5/70 12/69 10/69T 11/70 5/70 9/71 2/71 2/71 3/71 12/71 12/71 11/71

1971-1972 P 8/71T 9/72 1/72

1973-1975 P 1/73 4/73 1/73 2/73 1/73 11/73 1/73 6/74 2/73 8/73 10/73 8/73 10/73T 3/75 1/75 12/74 3/75 5/75 4/75 1/75 3/75 2/74 2/74 6/75 1/75 11/74 1/75

1975-1978 P 2/76 5/76 4/76 7/76 7/76 1/77 7/76 12/76 2/76 4/76 8/76 12/75 1/76T 9/77 7/77 4/77 10/77 6/77 7/77 3/77 10/77 8/77 1/78

1978-1980 P 4/79 5/79 6/79 12/79 1/80 2/80 2/79 1/78 8/78 1/79T 6/80 5/80 6/80 5/80 3/79 11/80 12/79 10/80 8/80

1980-1983 P 1/81 1/81 4/82 3/80 7/81 11/80 4/81 12/80 7/81T 7/82 7/82 1/83 10/82 9/82 9/81 8/82 6/81 5/83 2/83 3/82 5/83 5/82 1/83

1983-1985 P 1/84 11/83 10/83 8/84 5/83 7/84 12/84 8/84 10/83 11/83 1/84T 11/84 8/84 5/84 12/85 1/85 8/85

1985-1986 P 1/85 11/85 5/85 1/85 9/85T 11/86 8/86 12/85 4/86 6/86 7/86 10/86 1/86 8/86

1986-1987 P 4/86 5/86 10/86 8/86 11/86 9/86 11/87T 1/87 1/87 3/87 3/87 10/87

1987-1988 P 12/87 11/87 8/87 4/87T 7/88 11/88 2/88 1/88 7/88

1988-1989 P 1/88 2/88 1/88 2/88 10/88 2/88 6/88 9/88 8/89T 5/89 6/89 3/89 4/89

1989-1991 P 8/89 5/89 5/89 3/90 10/90 3/90 1/90 1/89T 2/91 2/91 4/91 10/91 6/91 9/91 4/91 1/90 4/91 2/91

1991-1994 P 1/91 3/92 4/93 4/92 6/91 10/91T 8/93 1/93 5/93 11/92 2/93 10/92 4/93 12/93 11/93 7/93 12/92 1/94 7/92

1994-1995 P 5/94 11/94 6/94 12/94 7/94 12/94 10/94 12/94 10/94 11/94 10/94T 4/95 8/95 10/95 7/95

1995-1997 P 1/95 2/96 11/94 2/96 4/95 1/95 1/95T 1/96 6/96 1/97 9/96 3/96 8/96 11/97 2/97 10/96 8/96 6/96

1997-1999 P 1/98 7/97 6/97 3/98 7/97 1/98 11/98 3/97 7/98 7/97T 9/99 7/98 1/99 2/99 2/99 9/98 1/99 4/98 7/98 1/99 5/98

2000- P 4/00

SOURCE: Economic Cycle Research Institute, New York City NOTE: Shaded cells represent periods for which data are not available.

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Biographies

Anirvan BANERJI is Director of Research of the Economic Cycle Research Institute (ECRI),

where he served earlier as Co-Director of Research with Geoffrey H. Moore. Prior to that, for

over a decade, he worked with Moore at Columbia University’s Center for International

Business Cycle Research (CIBCR). His recent publications, primarily in the area of economic

cycle analysis, have appeared in the International Journal of Forecasting, the Journal of

Economic and Social Measurement and Business Economics.

Lorene HIRIS is Professor of Finance at the C.W. Post Campus of Long Island University. She

is also Senior Research Scholar at ECRI where her research and publications focus on the

cyclical behavior of credit, inflation, and trade. Previously, she worked with Geoffrey H. Moore

as a Research Associate at CIBCR.

a multidimensional framework for measuring business...

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