An Examination into the Predictive Content of the Composite Index of Leading Indicators

MA Exit Paper Delehunt 0

An Examination into the Predictive Content of the Composite Index of Leading Indicators

Masters Degree Exit Paper Miami University

Sean Delehunt Wednesday, July 28, 2004


ABSTRACT

This paper sets out to determine from where the Composite Index of Leading Indicators (CLI) derives its predictive power. The inspiration comes from previous research which is discussed in the second section of the paper. Data have been obtained from a variety of sources on key macroeconomic variables, such as output, price level, interest rate, money supply and government spending, as well as the CLI and its ten components spanning from 1959 to 2004. The predictive power of the CLI components is examined both in an overall and marginal sense, through running several regression specifications and testing joint hypothesis. The results suggest that nearly all components currently included in the CLI offer overall predictive power to GDP, and half of these also offer marginal predictive power.


I. Introduction

Key economic indicators play an important role in understanding business cycles. Of

particular interest are leading economic indicators, which historically peak and trough before

business cycle peaks and troughs. These indicators have the potential to predict economic

recessions and expansions, as well as forecast numerical values of key macroeconomic variables.

Due to their potential predictive power, these indicators are of great economic and political

importance. Leading indicators have been in existence since the Great Depression, and they

were first complied into an index in 1968, using twelve of the most promising indicators. The

Composite Index of Leading Indicators (CLI), as it is known, has evolved over time as a result of

changes in the economy and the effectiveness of included indicators. Originally intended to

predict business cycle turning points, it is also now used to forecast values of important

macroeconomic variables.

This paper sets out to explore the predictive power of the leading indicators within the

CLI with respect to several key macroeconomic variables. In doing so, the history and

importance of leading economic indicators are explored by reviewing key empirical publications

on the topic. Additionally, several issues that arise when using leading indicators will be

illustrated. Once the proper background information is given and the importance established the

paper proceeds in its own empirical study, inspired by previous research, to determine from

where the index of leading indicators derives its predictive power. The structure is as follows.

Section II reviews the previous literature in this area and motivates the analysis of this paper.

Section III outlines the selected data, stationarity tests and variable transformations. The models

are introduced in Section IV and the results are discussed in Section V. Finally Section VI

concludes.


II. Previous Literature

II. a. Background on Leading Indicators

Leading economic indicators are a product of the Great Depression, created in an attempt

to foresee future economic activity and predict the flow of business cycles. In 1937 at the

request of Treasury Secretary Henry Morgenthau, Jr. members of the National Bureau of

Economic Research (NBER) Wesley C. Mitchell and Arthur F. Burns complied a list of

economic indicators that, in their words “have been tolerably consistent in their timing in relation

to business cycle revivals and that at the same time are sufficiently general interest to warrant

some attention by students of current economic conditions.”1 These leading indicators were

originally developed as a tool to predict business cycle turning points, rather than to forecast

levels of economic output; however their use evolved as econometric methods and computer

technology improved. The management of these leading indicators has also evolved over time.

Starting with the efforts of Mitchell and Burns at the NBER in compiling a list of leading

indicators, these indicators were compiled into an index in 1968 and maintained by the United

States Department of Commerce up to the mid-1990s. Since December 1995, The Conference

Board has been the official source of the CLI, as well as composite indexes of coincident and

lagging indicators. The composition of this index has also changed as more suitable indicators

have replaced those which were less effective.

These leading indicators have been complied into a single index for a variety of reasons.

First, the index provides a summary of the most important leading indicators. Additionally,

individual indicators perform differently depending on the situation; therefore an index of these

various indicators provides a well rounded prediction of economic activity. Also, incorporating

1 Mitchell and Burns 1938, quoted in Moore, 1979, p. 401.


these series into an index smoothes out the volatility of individual series and allows for good

predictions of the business cycle.

The index is created by taking a weighted average of the individual components and is

currently relative to a base of 100 in the year 1996. The issue of timeliness of data availability

when constructing the CLI is of concern to those who study it. Often times the CLI undergoes

substantial revisions as more accurate data are obtained, however these revisions take time.

Currently the CLI contains data on:

1. real money supply 2. stock prices 3. interest rate term spread 4. consumer expectations 5. new housing starts 6. average weekly manufacturing hours 7. average weekly initial claims for unemployment insurance 8. manufacturer’s new orders for consumer goods and materials 9. manufacturer’s new orders for non-defense capital goods 10. vendor performance

The Conference Board believes these to be the best combination of leading economic data for

predicting business cycles in today’s economy.

In addition to the official Composite Index of Leading Indicators maintained by The

Conference Board, James H. Stock and Mark W. Watson, have proposed additional experimental

indexes which they believe offer more accurate predictions of business cycle behavior. These

indexes are regularly released in the “Stock and Watson Indicator Report.”2 Their work on this

and other in areas is discussed the subsequent subsection.

II. b. Empirical Research with Leading Indicators

Much research has been done in the area of leading economic indicators and economic

forecasting due to the social and political importance of understanding and foreseeing business 2 Additional information about the Stock and Watson Indices is available at http://ksghome.harvard.edu/~.JStock.Academic.Ksg/xri/


cycles. This subsection highlights articles of particular interest in the area of leading indicators

and their explanatory power.

A starting place for any research into leading economic indicators is the work of James

H. Stock and Mark W. Watson (S&W) as they have focused much of their empirical efforts on

economic indicators. In their 1989 paper, they examine the then current index of leading

economic indictors and propose a new index of their own creation. S&W propose an alternative

index to that of the Department of Commerce (DOC), selecting indicators based on performance

and economic theory. They note that some variables survived their screening process based

solely on their implied importance from economic theory. Using historical data, they compare

the performance of the DOC index to their new index and find that their proposed index offers

substantial improvements over the current DOC index.

The proposed index contains variables not included in the DOC index which offer strong

predictive powers, these being the private-public interest rate spread and the change in the 10-

year Treasury bond yield. Additionally S&W note several indicators included in the DOC index

which “have little marginal predictive content”. These indicators include measure of money

supply, employment, consumption, inventories, investment, and stock market variables. This is a

somewhat surprising conclusion based on the importance of these variables in macroeconomic

theory. Rather than focusing on finding where the predictive power of the then current index

comes from, S&W went about creating their own index which they believe to have more

predictive power than the DOC index. Keeping these conclusions in mind several recent articles

have illustrated the importance of leading indicators in explaining and predicting business cycle

fluctuations.


In their 1990 essay, Victor Zarnowitz and Phillip Braun (Z&B) examine the role of key

macroeconomic variables and leading indexes in explaining business cycles in three time

periods: Pre-World War I, Interwar, and Post-World War II.3 They use the composite index of

leading indictors due to the fact that several past studies find a “relatively close and stable

relationship between changes in this index and changes in macroeconomic activity.”4 Z&B

justify their inclusion of the leading index for two reasons. First, they state that movements in

the leading index in a broad sense can be seen as a representation of the collective early

outcomes of investment, production, and, to some degree, consumption decisions. Secondly,

including this index in their analysis helps to overcome omitted variable bias as this index

represents a number of significant factors that would otherwise be omitted. Unfortunately, in

their study the index of leading indicators for the prewar period was not found to be significant;

however, Z&B attribute this to data problems and how the index was constructed for this period.

The results of the leading indexes used in the interwar and especially the postwar period were

much more promising. For the postwar period, Z&B find the rate of change in the leading index

to have the most statistically significant effect on the rate of change in real GDP when compared

to other variables used. Overall their study lends support to the importance of the leading index

in explaining GDP fluctuations.

As mentioned above, the original purpose of leading indicators was to predict turning

points in business cycles, rather than to create linear forecast models. James D. Hamilton and

Gabriel Perez-Quiros (1996) (H&PQ) note that anticipating a turning point is conceptually

different from minimizing the mean squared forecast error, and an index of leading indicators

may be useful for one of these tasks but not necessarily the other. H&PQ test to see if the CLI is

3 Indices for the pre and inter war periods were constructed with the best available data. 4 Braun and Zarnowitz (1990) p.357.


most useful at predicting cyclical turning points or in use in linear forecasting models.

Additionally, they test for and take into account the cointegration between the CLI and GNP,

which arises as a result of how the CLI is constructed. They find the CLI to be a useful tool in

predicting turning points as well as forecasting GNP, and simple linear forecasting to be as good

as their proposed nonlinear models.

In their analysis, they comment on the timeliness of the CLI. The CLI issued one period

is constantly updated in subsequent periods as more accurate data are obtained or when

definitional changes are made. Therefore more accurate values of the CLI for a given month

may come out months later, and this information may no longer be useful for predicting the next

quarter’s GNP. Due to this informational lag, H&PQ warn that the CLI may not be a practical

forecasting tool. Even so, in their real-time exercise they find the CLI to be useful in forecasting

GNP. Additionally, they find the CLI helps in identifying the beginning and end points of a

recession. In testing for cointegration of order one between the CLI and GNP in growth rates,

they strongly reject the null hypothesis of no cointegration, and when accounting for the

cointegration in their previous analysis, their results are only strengthened. Overall H&PQ find

the CLI to be a useful tool in both forecasting GNP and business cycle turning points, and that

these forecasts are best obtained with a simple linear relationship between GNP growth and CLI

growth.

Several recent papers have analyzed the performance of leading indicators in the 2001

recession. Stock and Watson (2003) evaluate the performance of professional forecasts based on

the leading indicators and the predictive power of the individual indicators in this recent

recession. Using data from the Survey of Professional Forecasters, conducted monthly by the

Federal Reserve Bank of Philadelphia, they compare professional forecasts of GDP growth to the


actual GDP growth figures. Their findings suggest that this was a difficult recession for

professional forecasters to predict. In an attempt to explain why professionals had difficulty

foreseeing this recession, S&W analyze the performance of the individual indicators. To do this,

they run an autoregression which includes GDP growth and a chosen indicator, one

autoregression for each of the leading indicators. They compare the mean squared error of each

autoregression to that of a benchmark GDP growth autoregression. From this, they found

interest rate spreads, stock prices and new claims for unemployment insurance outperformed the

benchmark based on the result that they had lower mean squared errors than the benchmark.

Previously reliable indicators, such as residential building permits and consumer confidence

performed worse than the benchmark.

S&W believe every recession is unique and therefore leading indicators do not perform

consistently from one recession to the next. They note that this conclusion may be disappointing

but not surprising. Since its inception economists realized that recessions differ and suggested

including a variety of indicators in the index for this reason. While there may not be any leading

indicator that proves reliable over time, one hopes at least some of the components will perform

strongly throughout business cycles, so the index as a whole is useful.

Filardo (2004) examined how several recession prediction models preformed in

forecasting the 2001 recession, noting that this is an interesting recession to examine due to its

mildness. He focused on four specific models, traditional rule-of-thumb models, Neftci’s

sequential probability model, a probit model, and Stock and Watson’s experimental recession

indexes, all of which incorporate components of the CLI. Filardo finds that the first three models

are reasonably effective at showing signs of an impending recession, although the basic rule-of-

thumb models which use monthly changes in the CLI give numerous false signals. The Stock


and Watson experimental recession indexes failed to perform well in this case and Filardo

mentions that they have been criticized for placing too much importance on financial variables

and interest rates instead of traditional CLI variables. This is an interesting result, as S&W had

previously thought their experimental indices to be superior to the CLI. In conclusion, he hopes

that his findings may renew interest in using the CLI indicators in the analysis of business cycle

fluctuations, and additionally that the theory and construction of the CLI will continue to shape

the understanding of business cycles well into the future.

Kevin L. Kliesen (2003) also examines the uniqueness of the 2001 recession and the

performance of various economic indicators and forecasts. He collected data on various

economic series for previous post-war recessions and compares these to the values of the same

series in the 2001 recession. This data comes from Blue Chip Economic Indicators, which

contain data on quarterly forecasts. Kliesen examines forecast errors from a macroeconomic

forecasting model. He concludes that low interest rates helped the economy by allowing interest

sensitive activity, such as new home building and sales, to remain strong, and also that the sharp

declines in business capital spending were due to falling equity prices during the recession.

Dueker (2002) investigates the effectiveness of CLI based forecasts in the 2001 and

1990-91 recessions using various probit models. Forecasting models largely missed predicting

the 1990-91 recession, and Dueker’s study agrees with this common conclusion. Additionally he

finds the 2001 recession to be difficult to predict using the simplest of his three probit models,

which uses percent change in the CLI to predict the probability that the economy will be in a

recession in a specified amount of time. He uses two more models that account for the

probability that the recession occurs in different regimes (in this case low volatility or high

volatility regime). These models did a much better job in predicting an imminent recession,


which lends support to the belief that changes in the business cycle will affect the predictive

power of CLI components and should be accounted for.

In 2003, Timotej Jagric took a neural network approach5 to construct a new forecasting

model based on leading indicators. The main reason for using neural networks in forecasting is

for questions of functional form, which has been a common theme in forecasting literature.

Jagric looks at the forecasting performance of neural network models since he, along with others,

believes traditional linear models may not adequately describe business cycles. He uses data on

leading indicators for Slovenia for the years 1993 to 2001. While this article does not focus on

U.S. leading indicator data, it lends support to the importance of leading indicators in a larger

context. The neural network model detected all turning points, an important feat that many

forecast models struggle to accomplish successfully. Additionally, this new model overcame

several major deficiencies by correctly forecasting all reference points in in-sample and out-of-

sample data, forecasting future values of the reference series (in contrast to classical leading

indicators models), and the model had a fixed forecast horizon of twelve months.

H.O. Stekler (2003) advises using caution when interpreting movements in the CLI,

noting that many predictions made using the CLI generally miss the turning points. Using

historical data on the CLI to evaluate forecasting performance throughout the past decades, he

finds that forecasts made with real-time versions of the indicators would have failed to provide

any signal of cyclical downturns before they occurred. He questions the ability of the CLI to

actually lead business cycle peaks and troughs. Forecasts using revised historical data however

showed much more promise in predicting turning points; however, this is of little comfort since

often times the revisions are made too late to be used in forecasting. Overall, these disappointing

5 Neural networks are used to determine the best function form. The functional form is selected based on best fit and not necessarily grounded in theory. For additional information see Jagric (2003).


findings lead Stekler to conclude the CLI might not be as valuable a forecasting tool as some

have believed.

The timeliness of the release of an accurate CLI is a continual concern in the forecasting

literature. A problem with the current procedure for calculating the CLI is that it fails to use the

most up to date information for some components. Indicators included in the CLI can be broadly

classified in two main groups: financial variables and real macroeconomic variables. The real

macroeconomic variables are usually released with a one period lag, as it takes time to compile

the information. On the other hand, financial variables, such as stock prices and interest rate

spreads, are available in real time. McGuckin, Ozyildirim, and Zarnowitz (2001) (MOZ) give

the example of the index of leading indicators that is published in March uses January data,

despite the availability of February data for some of the indicators. They cite this as a possible

cause for the poor performance of the CLI in recent studies. As an alternative, MOZ use the

most up to date information for financial variables and create autoregressive forecasts of the real

macroeconomic variables to bring these variables up to the current period. It is important for all

components in the index to be measures from the same period. Using this alternatively

constructed index, MOZ find that it consistently outperforms the current index for a historical

sample covering the period 1970 to 2000. They feel this new method offers substantial ex-ante

forecasting improvements over the current method.

An additional problem that forecasters may not currently be able to work around is the

inexactness of real macroeconomic indicators and the lag time for their revisions to occur. Due

to this, several economists have proposed indexes which are comprised entirely of financial

variables; however, MOZ warn that performance of financial variables in forecasting varies

across business cycles, and for this reason a more complete index should be used.


Ruey S. Tsay and Chung-Shu Wu (2003) touch on the timeliness of indicators and

accommodations of the changing environment when forecasting with leading indicators. They

note the economy has undergone numerous advances, especially in computer technology, which

allow for improved data collection and faster informational availability. Due to these structural

changes, which are discussed in the following subsection, Tsay and Wu (T&W) suggest that

frequency of the publication of leading indicator data may be too sparse to effectively capture

movements in the economy; however this is a problem beyond their control. They make use of a

functional-coefficient transfer function model, where the model parameters evolve though the

use of a state variable. This state variable represents changes in economic conditions, and allows

for a changing relationship between the variables as the economy changes state. Using a time

index as the state variable, which they feel proxies for technological development, T&W find

their model outperforms others. As a result of their study, they believe when forecasting with

leading indicators, environmental changes should be taken into account.

These articles lend support to the importance of leading economic indicators in

forecasting business cycles as well as the concerns some have with using them and their

timeliness. It has been shown, however, that if used in the right manner, leading indicators can

yield impressive results in forecasting economic activity.

II. c. Has the Business Cycle Changed and What Effects Does This Have on Leading Indicators?

Problems in the predictive power of leading indicators are perhaps a result of some

fundamental change in the business cycle. While the index is monitored and revised when

necessary, it may be difficult to determine if a perceived change in business cycles is permanent.

As a result of the boom of the 1990s and the quasi-uniqueness of the 2001 recession, many

economists have offered their input as to whether or not there has been a fundamental change in


the business cycle. Several papers mentioned above concentrated on the inability of professional

CLI based forecasts to predict the 2001 recession.

Victor Zarnowitz, an expert on business cycles, has prepared several recent papers on the

topic of a fundamental change in the business cycle. The long business expansions enjoyed in

the past decades have created expectations of unending economic prosperity; however these

beliefs appear unfounded when taking into account historical information. Zarnowitz (1998)

offers his insight into the belief that business cycles may have been eliminated. In order for

indefinitely long business expansions to occur, he believes real growth must be moderate, prices

of goods and services must rise slowly, and the prices of stocks must be allowed to rise

indefinitely with only minor setbacks. The fact that there have been no deflationary periods

since World War II plays a key role in stabilizing the economy; however there have been periods

of high inflation which has increased uncertainty and hampered economic growth.

Unfortunately, the prices of financial assets appear to be much more volatile, and their

stabilization requires intervention by the Federal Reserve, which has been reluctant to do so.

Zarnowitz warns that too much attention has been placed on single shock causes of cyclical

downturns, such as oil prices in the 1970s and monetary policy shifts in the early 1980s, rather

than overall economic conditions. In past recessions where explanatory emphasis has been

placed on a single shock, these recessions were preceded by declines in overall economic activity

prior to the shock, suggesting there was not a mono-causal source of the recession.

Overall, Zarnowitz states that expansions have become longer, and recessions much

shorter since World War II; key reasons for this being no deflationary periods, a shift of

employment to less cyclical service industries, automatic stabilizers, the creation of the FDIC to

prevent bank failures, and less volatile monetary grow, all of which feed into increased


consumer, business and investor confidence. However, at best these factors can only limit

business cycle volatility, not eliminate the cycle entirely. Taking this into account, the business

cycle has changed for the better; however, it is not easier to predict. Zarnowitz notes that in

forecasting, economists often miss cyclical turning points, especially the peaks. The concern

arises from what Milton Friedman refers to as the “tyranny of the majority,” where it is in an

individual forecaster’s best interest to go along with the predictions of others. This is an

unfortunate side effect of having a large number of professional forecasters.

Zarnowitz (1999) provides a history of business cycles over the past century, focusing on

what influences them, and comparing trends over time. His motivation is the speculation that the

1990s are the beginning of a new era of economic growth, based on exuberance about economic

stability and innovations in computing technology throughout the decade. He therefore

compares this expansionary period to past expansions in the 1960s and 1980s. Zarnowitz

evaluates the effects of profits, investment, and credit on business cycles. Profits are evaluated

using data from The Conference Boards Business Cycle Indicators for the years 1953 to 1998,

and he uses regression analysis to determine which indicators affect profits and in what manner.

He then does the same for investment and change in investment. This allows him to compare

business cycle conditions over time. Comparing the 1990 expansion to those in the 1960s and

1980s, he notes that overall rise in real GDP and employment was much larger then than in the

1990s. As an additional warning for the United States, Zarnowitz cites examples of growth

turned bad in Asia.

Overall, he concludes that since the 1970s and 1980s, business cycles have become more

moderate, so some of the exuberance is not unfounded. His advice is that people continue to

observe the patterns of endogenous domestic variables that play major roles in foreign business


cycles, as they are likely to have similar effects in the United States. The occurrence of the 2001

recession and ensuing jobless recovery helped to quell beliefs that business cycles have ended.

While the recession was relatively short and mild, high levels of unemployment have dampened

enthusiasm. Nevertheless, it is important to realize that structural changes and policy

implementations have had a lasting effect on the U.S. economy.

Stock and Watson (2002) also investigate the postulated change in the business cycle, and

find a widespread moderation in volatility in the 1990s as expected. They cite increased stability

of residential investment, the output of durable goods, and the output of structures as the three

most important components in the overall drop in volatility. S&W date the decline in GDP

volatility to the mid-1980s. They find various causes for the increased stability; stating that the

improved monetary policy of the Federal Reserve accounts for 10 to 25 percent of the decline in

output volatility. Additionally, some of the decline can be attributed to less volatile productivity

and commodity price shocks; however much of the decline in volatility is unaccounted for based

on their findings. Previously praised changes, such as the shift from manufacturing to service

and improvements in inventory management, did not seem to provide sufficient explanation for

the decline in GDP volatility. S&W do note a connection between decreased volatility and

increased precision of economic forecasting; however, they cannot account for any specific

source for improved forecasting. Overall, they conclude that the past fifteen years have

exhibited moderate volatility, but much of this seems to be in the form of good luck. The lack of

major economic disturbances may not continue, and S&W feel the U.S. economy could return to

“more turbulent times”.

These papers illustrate that even though the U.S. economy seems to be better off than

ever, it can still plunge into recession. On an encouraging note, it appears structural changes


since World War II have had a positive effect on the economy as a whole, although there have

been several occasions of economic turmoil since then. Luckily with improved policy and the

lack of major shocks to the economy, economic growth has been able to proceed in a relatively

stable manner. Nevertheless, forecasters and those compiling indexes should keep in mind the

uniqueness of individual business cycles, as well as the overall trends, and not simply use what

has worked best in most recent history.

II. d. Future Research and Motivation

In an area of such great social and political importance, there is clearly room for future

research. Evidence of structural changes to the business cycle, and the recent work of Tsay and

Wu, which examined the role of changes economic environment when forecasting, suggest the

importance of accounting for these economic changes forecasting with leading indicators.

Indeed further research into this area is in order. In general, research into the most effective

methods of using indexes of leading economic indicators to forecast business cycles is merited.

Jaric (2003) was able to obtain impressive results using a neural network forecasting model.

Some economists question the validity of these methods as they lack a basis in economic theory,

however if proven to be effective predictive tools they are sure to be of great benefit. On the

other hand, some forecasters achieved sound results with linear models. Surely the question of

functional form will continue to play an important role in forecasting with leading indicators.

Additionally, work can be done by the Conference Board with the Composite Index of

Leading Indicators, most notably with its timeliness. The research of McGuckin, Ozyildirim and

Zarnowitz (2001) offers a viable method of incorporating the most up to date information in the

creation of the CLI. In light of technological advancements which help to facilitate the

collection of data, one should expect the lag time of many real macroeconomic variables to


diminish in the near future. Hopefully the Conference Board can make use of advances in

technology in the best possible way to expedite the release of the CLI, although the tradeoff

between timeliness and quality should not be forgotten.

Finally, Zarnowitz and Braun (1990) offer several suggestions for further research, one of

these being of specific interest; they propose trying to determine where the explanatory power of

the CLI comes from. Of the various paths for further research in the area of leading indicators,

this is the on this paper will pursue. Although the importance of using the index as a whole has

been illustrated above, it is a useful exercise to evaluate the performance of individual indicators.

These indicators have been selected for use in the CLI for their ability to precede movements in

the overall economy. In determining the source of predictive power for the index, one can better

understand its ability to predict turning points and forecast values of key macroeconomic

variables and perhaps understand why its ability to predict variations in the business cycle

changes over time.

III. Data

III. a. Selected Variables

Time series data has been obtained for the index of leading economic indicators and

several other key macroeconomic variables. All data are quarterly and span from the first quarter

of 1959 to the first quarter of 2004. Information about the sixteen selected variables is

summarized in Table 1.


Table 1: Variable Names, Symbols and Sources No. Variable Name Form Symbol Sourcea Notesb

1 Real GDP ∆ln y FREDc Billions of Chained 2000 Dollars, SAAR.

2 GDP Deflator ∆ln p FREDc Index 2000 = 100, SA. 3 Treasury

Constant Maturity ∆ r FREDd Average Of Business Days, in Percent,

Freq. Converted from Monthly. 4 Nominal M2 ∆ln nm2 FREDd Billions US Dollars, Freq. Converted

from Monthly, SA. 5 Federal Current

Expenditures ∆ln g FREDc Billions of US Dollars, SAAR.

6 CLI ∆ln cli DRIe Composite Index Of Leading Indicators, 1996=100, SA.

7 Real M2 ∆ln rm2 DRIe Money Supply, M2, 1996 Dollars, SA.

8 Stock Price Index ∆ln sp DRIe S&P 500 Common Stock Price Index: Composite, 1941-43=10, NSA.

9 Interest Rate Term Spread

level term DRIe 10-Yr Treasury Bonds Less Fed Funds; % Per Annum, NSA.

10 Consumer Expectations

ln level cexp DRIe U of Michigan Index of Consumer Expectations, 1966:2 = 100, NSA.

11 New Housing Starts

ln level house DRIe Housing Authorized: Total New Private Housing Units, in thousands, SAAR.

12 Manufacturing Hours

ln level mfg BLSe Average Weekly Hours of Production, Workers: Manufacturing, SA.

13 New Claims for Unemployment Insurance

∆ln unemp DRIe Average Weekly Initial Claims, State Unemployment Insurance (except Puerto Rico), in Thousands, SA.

14 New Goods Orders

∆ln goods DRIe New Orders (Net) - Consumer Goods & Materials, Billions of 1996 Dollars, SA.

15 New Capital Orders

∆ln cap DRIe New Orders, Non-defense Capital Goods, Billions of 1996 Dollars, SA.

16 Vendor Performance

level vendor DRIe NAPM Vendor Deliveries Index, in Percent, SA.

a. FRED is the Federal Reserve Economic Data (http://research.stlouisfed.org/fred2/), DRI is the Basic Economics Database (2004), published by DRI-WEFA, Inc, BLS is the Bureau of Labor Statistics.

b. SA = Seasonally Adjusted, SAAR = Seasonally Adjusted Annual Rate, NSA = Not Seasonally Adjusted. c. Series maintained by the Bureau of Economic Analysis. d. Series maintained by Board of Governors of the Federal Reserve System. e. Series maintained by The Conference Board.


Variables one though five in Table 1 account for key macroeconomic variables: output,

price level, interest rate, money supply and government spending, and have been obtained from

the Federal Reserve Bank of St. Louis Economic Data. The remaining variables are the

Composite Index of Leading Indicators (CLI) and its ten individual components. These data are

maintained and published by The Conference Board and have been obtained from the 2004 DRI

Basics database.6 Components included in the CLI have been specifically selected since they

historically turn before business cycles and therefore offer important information about

movements in the aggregate economy. A full description of and justification for the ten

components of the CLI can be obtained from The Conference Board’s official indicators

website.7

III .b. Unit Roots and Variable Transformations:

When working with time series data it is important to check for the presence of unit roots

before proceeding to statistical modeling. Neglecting to account for non-stationary variables can

result in biases caused by spurious correlation between the variables. The Augmented Dickey-

Fuller (ADF) procedure is used to test for the presence of unit roots. The Dickey-Fuller test is

only appropriate for first order autoregressive processes, whereas the ADF Test can handle

higher order correlation due to the presence of lagged first difference terms. The test is

conducted by performing the following regression:

(1) t

N

nntit ytayay εβγ +∆+++=∆ ∑

=−−

1210

where y is the specific time series variable in question, a0 is the constant term, t is the time trend,

and N = 4 quarterly lags. The null hypothesis is:

6 Data for average weekly manufacturing hours, could not be obtained from the 2004 DRI Basics database, and was therefore obtained directly from the Bureau of Labor Statistics. 7 http://www.tcb-indicators.org/GeneralInfo/component_description.cfm


0:0 =γH (2)

where failure to reject represents the presence of a unit root. The ADF Test statistic is a t-

statistic calculated by dividing the coefficient γ, by its standard error.8 This test is conducted for

each of the sixteen variables in Table 1. Test statistics are computed using Equation (1) with a

trend and constant terms (stationary around a trend) and with the coefficient on the time trend, a2

restricted to zero (mean stationary). These results are summarized in Table 2 below. ADF Test

statistics are listed for tests with a time trend and constant (ττ) and with a constant term only (τµ).

Table 2: Unit Root Test Statisticsa

I. Tests on Levelsc II. Tests on First Differencesc

Seriesb ττ τµ ττ τµy -2.89713 -0.84658 -5.67476* -5.58784* p -1.87996 d -1.59285d -2.23539 d -2.0276 d

r -1.76641 -1.82332 -5.33868* -5.23661* nm2 -0.73617 -2.04157 -3.49764+ -3.04664+

g -0.24383 -2.28579 -4.73395* -4.08576* cli -3.68138+ -1.44088 -6.02582* -5.98319* rm2 -2.97809 -1.60449 -4.00331+ -3.89312* sp -1.75422 0.131102 -5.9349* -5.89323* term -4.18999* -3.95674* -5.57132* -5.5801* cexp -2.90159 -2.9611+ -6.28165* -6.2732* house -3.66025+ -3.47394+ -5.44139* -5.46639* mfg -3.23397° -2.8939+ -6.39628* -6.41573* unemp -2.74393 -2.52149 -6.26745* -6.28615* goods -2.99081 -1.8993 -5.89226* -5.76336* cap -3.3486° -1.96558 -6.18339* -6.12914* vendor -4.9819* -4.87057* -7.29915* -7.32027*

a. ττ is the ADF test statistic for tests including a constant plus time trend, τµ is the ADF test statistic for tests with a constant but no time trend.

b. For variable definitions see Table 1. All variables are in natural logs except r, term, and vendor. c. Significance at the 1% level is represented by *, at the 5% level by +, and at the 10% level by °. d. While the price level, p is not stationary in either levels or first differences under the ADF Test, it is

stationary according to the Phillips-Perron Test (with constant and without trend) in first difference form with a test statistic of -3.00116, which is significant at the 5% level.

8 Critical Values are tabulated in Enders (2004) Table A, p. 439, reproduced from Fuller, W. A. 1976. Introduction to statistical time series. New York: Wiley.


While variables such as term, cexp, house, mfg, and vendor are all stationary in levels, in

some cases transformations were necessary to create stationary variables. Part II of Table 2

shows that almost all series are stationary using the ADF test on first differences. Therefore y, r,

nm2, g, cli, rm2, sp, goods, and cap have all been first differenced for use in further statistical

analysis. Normally price levels are stationary in either levels or first differences; however the

price level chosen in this analysis, the GDP implicit price deflator, is not stationary in either form

over the selected range when using the ADF test. While not stationary under this traditional test,

the series is stationary in first difference form when using the Philips-Perron Test for unit roots at

the 95 percent confidence level. The Phillips-Perron Test has been deemed acceptable for the

purposes of this study and the first difference of the price level is used in the subsequent analysis.

Additionally, unemp is nearly stationary at the 10 percent level. This variable is “on the fence”

between being stationary in levels and being stationary in first differences. In the ensuing

analysis, the form used has no considerable effect on the results; however, the ADF Test

indicates the first difference form should be used and therefore these are the results reported in

Section V.

IV. Model

A standard vector autoregression (VAR) composed of output, inflation, money supply,

interest rate, and government spending, is used as the baseline statistical model. The model is in

the form of equation (3) below:

(3) t

J

jitjij

I

it exx ++= ∑∑

=−

= 1,

10 αα

where xt is a ( vector containing variables one through five listed in Table 1 above, α)

)

15× t is a

vector of intercept terms, I = 4 quarterly lags, J = 5 endogenous variables, and e( 15× t is a


( 15× ) vector of error terms. A time trend was initially included in Equation (3) as well, however

in many cases it was not statistically significant and its presence had no significant impact on the

results. Due to these factors, it has been omitted and all results reported are for specifications

without a time trend.

In order to test the explanatory power of the individual components in the CLI, four

quarterly lagged values of the CLI or one of its individual components were added to

Equation (3) above, resulting in the following transformation:

(4) ( ) t

J

jtjijitjij

I

it eCLIxx +++= ∑∑

=−−

= 11,,

10 βαα

where CLI represents either the composite index of leading indicators as a whole or one of its

individual components. Given that there are ten components and the overall index, eleven

variations of Equation (4) are estimated using ordinary least squares.

Using a specification like Equation (4) helps to eliminate biases caused by omitted

variables. For each dependent variable, xt, predictive power from its own lagged values and

from the other key macroeconomic variables is controlled for. Therefore when a particular

component is added, this component is not picking up the effect of other macroeconomic

variables. In explaining output, for example, the predictive power of its lagged values, as well as

that of lagged values of the price level, interest rate, nominal money supply and government

spending are accounted for. When a CLI component is added and found to offer predictive

power of output, this predictive power can more accurately be attributed to the component itself,

rather than other key macroeconomic variables.

While the first part of this analysis looks at the overall predictive power of CLI

components, the second part examines the marginal predictive power of the components. This

can help to give an idea of whether or not some components offer more predictive power than


others. For this analysis a transformation of Equation (3) is used which includes all of the CLI

components in the regression, rather than just one at a time as in Equation (4).9

(5) ( ) t

J

jtjijtjijitjij

I

it eCLICLIxx +++++= ∑∑

=−−−

= 11,101,1,

10 ... ββαα

Equation (4) tests for the overall predictive power of an individual component, whereas Equation

(5) examines the question in a different way: given the regression includes nine of the CLI

components, when the tenth is added, is there any additional predictive power? Looking at

marginal predictive power examines the question, does this component really belong in (i.e. add

to) the CLI?

The test for explanatory power, either overall or marginal, from an individual component

is an F-statistic which tests the null hypothesis:

0...: 410 === −− tt CLICLIH (

6)

against the alternative hypothesis that the coefficients on all lagged values of a component are

not equal to zero. In order for a component to have predictive power over the selected dependent

variable, at least one of the four lagged values must be statistically different from zero. In

checking for overall predictive power, this test is conducted for the index and all ten components

for each of the five dependent variables. The test to see if a component adds predictive power,

after the other nine components have been accounted for is performed ten times, once for each of

the individual components. Results for both analyses are reported in the following section.

9 The CLI itself is not included in the second analysis, as it would then pick up some of the effects of the individually tested component.


V. Results

V .a. Overall Predictive Power

The values of the coefficients from the individual regressions are of little interest in this

study, and as a result are not reported. What is relevant is the joint significance of the four

lagged values of each individual component, as this illustrates its predictive power on the

selected dependent variable. Table 3 lists the results of the joint hypothesis test on the four

lagged values of the CLI and its components for each of the five dependent variables. A “yes”

indicates that the selected component adds predictive power to the specified dependent variable,

i.e. a rejection of the null hypothesis. The statistical significance of a “yes” is denoted by it

superscripted symbol. A “no” indicates failure to reject the null hypothesis and therefore no

added predictive power from the selected component on the specified dependent variable.

Of particular interest is the explanatory power of an individual component on y, as it is a

measure of overall economic activity, and the index was designed to predict economic turning

points. It is surprising to see that nearly every component in the CLI offers statistically

significant explanatory power to real GDP. Based on previous research, one might have

expected a few of the components to have strong predictive power of output, but probably not

expect to see nearly every component being accountable for the predictive power of the leading

index. Due to the manner in which the idea is proposed in Zarnowitz and Braun (1990), it seems

the authors would expect the predictive power of the index to be derived from a few key

components.

In addition to predicting levels of and changes in output, the CLI may offer explanatory

power to other important macroeconomic variables, such as price level, interest rates, money


vend

or

yes°

yes*

no

no

yes+

cap

yes+

yes*

no

no

no

good

s

yes°

no

no

no

no

unem

p

yes*

yes°

no

no

no

mfg

no

no

no

no

no

hous

e

yes*

yes*

no

no

no

cexp

yes+

no

yes°

no

no

term

yes+

yes+

no

yes+

no

sp

yes*

no

yes+

no

no

rm2

no

yes*

no

no

no

CLI

Com

pone

ntb,

c,d

cli

yes*

no

yes*

no

no

a.

See

Tabl

e 1

for v

aria

ble

defin

ition

s. b.

Ea

ch c

olum

n re

pres

ents

the

spec

ifica

tion

show

n in

Equ

atio

n (3

) plu

s the

CLI

com

pone

nt d

enot

ed b

y th

e co

lum

n he

adin

g.

c.

H0 :

CLI

t-1 =

… =

CLI

t-4 =

0

Yes

: Rej

ectio

n of

the

null

hypo

thes

is, a

t lea

st o

ne la

gged

val

ue o

ffer

s pre

dict

ive

pow

er.

N

o: F

ailu

re to

reje

ct th

e nu

ll hy

poth

esis

.

d.

Sign

ifica

nce

at th

e 1%

leve

l is r

epre

sent

ed b

y * , a

t the

5%

leve

l by

+ , and

at t

he 1

0% le

vel b

y °.

Tab

le 3

: D

oes t

he S

elec

ted

Com

pone

nt O

ffer

Ove

rall

Exp

lana

tory

Pow

er?a

Dep

Var

iabl

e

y p r

nm2 g


supply and perhaps even government spending. The results of the predictive power of the CLI

on these other macroeconomic variables are summarized in rows two through five of Table 3.

Many of the components have statistically significant predictive power on the price level,

p; however, the index as a whole does not. While one can expect a relationship between price

level and money supply, many of the other components may predict p, because they predict

nominal GDP. Nominal GDP is p times y, therefore components that affect y also have an effect

on p. This is the hypothesized reason behind the high number of components that have

statistically significant predictive power of the price level. While many components are

significant, the reason the index itself has no statistically significant predictive power may be

that the CLI is not composed in a manner to best predict price level.

The Treasury Constant Maturity Rate, r, is the only other key macroeconomic variable in

this study besides output which receives significant explanatory power from the CLI; however,

many of the individual components are not statistically significant. The explanatory power of

the CLI on interest rates is therefore derived from only one or a few individual components. The

most highly significant component in explaining interest rates is the S&P 500 index of common

stock prices, sp. Movements in this index reflect movements in interest rates due to the ceteris

paribus inverse relationship between interest rates and stock prices. Interest rates can be viewed

as the opportunity cost of holding stocks, and therefore stock prices can offer some predictive

power as to the movement of interest rates. The other component, significant as the 10 percent

level, is the index of consumer expectations, cexp. Consumer expectations can be heavily

influenced by interest rates. Higher expectations can be representative of lower interest rates, as

consumers are able to finance the purchase of durables under desirable terms. It is interesting


that the CLI has such statistically significant predictive power of interest rates, yet only two of

the components are statistically significant.

Only term has significant predictive power of Nominal M2 Money Supply. This should

not be surprising as the Federal Reserve controls the money supply and enacts a policy of interest

rate targeting. The spread can indicate inflation expectations as the 10-year Treasury bond rate

has inflation expectations built into it. As the spread widens it represents an increase in expected

future inflation which is related to money supply. Also, the Federal Reserve targets the federal

funds rate using monetary policy, so one should expect a close relationship between money

supply and the federal funds rate. The discount rate is closely related to the federal funds rate,

and therefore offers predictive power of nominal M2. The Conference Board itself says this

particular indicator is felt to be an indicator of the stance of monetary policy because it rises

when short term rates are low and falls when short term rates are high.10 Based on this, one

would expect to find this indicator to have some predictive power of the nominal money supply.

The CLI and its components have almost no predictive power of government spending.

This should not be surprising however, since often times government spending decisions are

made either independently of or opposite to macroeconomic activity. One might expect interest

rates to have some small influence on government spending; however, in this study it offers no

statistically significant predictive power. The only component to have any significant

explanatory power of government spending is vendor performance. As stated by The

Conference Board, “this index measures the relative speed at which industrial companies receive

deliveries from their suppliers.”11 Government purchases lead to an increase in demand for

manufacturing supplies, which in turn can lead to slower delivery of supplies if this increase

10 http://www.tcb-indicators.org/GeneralInfo/component_description.cfm 11 Ibid.


demand is not entirely foreseen. Additionally, at times when vendor performance is already

slowed, government purchases may be postponed. Therefore it is possible to imagine cases

where vendor performance can have predictive power over government spending. Overall it is

no shock that only one variable offers statistically significant explanatory power to federal

government spending, as federal purchasing decisions are generally not constrained by

macroeconomic conditions.

Also of notable interest is that average weekly manufacturing hours offers no statistically

significant explanatory power to any of the five dependent variables included in this study. One

would expect that a component included in the CLI would offer predictive power of at least one

of the key macroeconomic variables included in this study. Perhaps it is simply included due to

its theoretical significance.

V. b. Marginal Predictive Power

The test results for the marginal predictive power analysis are summarized in Table 4,

which is formatted similar to Table 3. As may be expected some components, which offered

overall predictive power do not offer statistically significant marginal predictive power and no

components which failed to offer overall predictive power were able to offer marginal predictive

power. Changes in predictive power from the overall test to the marginal test are indicated by a

superscripted delta. Again of key interest is the predictive power of CLI components with

respect to output, as the index was created to predict overall economic activity. From the eight

components which offered statistically significant predictive power in the overall analysis, only

four remain after the marginal analysis. These components can be seen as having more

predictive power than others, as they have passed this second round of screening. Two of those

surviving indicators are financial variables, stock prices and the interest rate spread, which have


vend

or

yes°

yes*

no

no

no∆

cap

no∆

no∆

no

no

no

good

s

no∆

no

no

no

no

unem

p

no∆

no∆

no

no

no

mfg

no

no

no

no

no

hous

e

yes+

yes*

no

no

no

cexp

no∆

no

no∆

no

no

term

yes*

no∆

no

yes*

no

sp

yes+

no

no∆

no

no

CLI

Com

pone

ntb,

c,d

rm2

no

yes*

no

no

no

a.

See

Tabl

e 1

for v

aria

ble

defin

ition

s. b.

Ea

ch c

olum

n re

pres

ents

the

spec

ifica

tion

show

n in

Equ

atio

n (3

) plu

s all

10 C

LI c

ompo

nent

s. T

he

hypo

thes

is te

st is

for 4

lagg

ed q

uarte

rly v

alue

s of t

he c

ompo

nent

den

oted

by

the

colu

mn

head

ing.

c.

H

0 : C

LIt-1

= …

= C

LI t-4

= 0

Y

es: R

ejec

tion

of th

e nu

ll hy

poth

esis

. N

o: F

ailu

re to

reje

ct th

e nu

ll hy

poth

esis

. °

d.

Sign

ifica

nce

at th

e 1%

leve

l is r

epre

sent

ed b

y * , a

t the

5%

leve

l by

+ , and

at t

he 1

0% le

vel b

y ° .

A

chan

ge in

pre

dict

ive

pow

er fr

om th

e ov

eral

l to

the

mar

gina

l tes

t is r

epre

sent

ed b

y ∆ .

.

Tab

le 4

: D

oes t

he S

elec

ted

Com

pone

nt O

ffer

Any

Mar

gina

l Pre

dict

ive

Pow

er?a

Dep

Var

iabl

e

y p r

nm2 g


both been successful predictors in previous research. The other two are new housing starts,

which failed to perform successfully in recent history and vendor performance, Chairman

Greenspan’s indicator of choice.

As for the other macroeconomic variables included, there is a similar trend of fewer

components having statistically significant marginal predictive power than in the overall

analysis. Real M2 money supply still offers predictive power to the price level; however, several

of the other components that previously had statically significant predictive power no longer do.

There are no changes in the predictive power of the components with respect to nominal M2. On

the other hand, no components offer any statistically significant marginal predictive power to

either the treasury interest rate or government expenditures.

VI. Conclusion

These results show that nearly all components included in the CLI offer predictive power

to real GDP when evaluated on their own; however when the other CLI indicators are accounted

for the number of indicators still exhibiting predictive power diminishes by half. All but the real

M2 money supply component and the average weekly manufacturing hours components are

statistically significant in explaining real GDP in the overall analysis. The presence of these

indicators in the CLI that failed to provide any overall predictive power should likely be

reevaluated. Additionally previous research has not showcased either real M2 money supply or

average weekly manufacturing hours as particularly strong indicators. Those indicators which

additionally demonstrated marginal predictive power most likely deserve their place in the index

of leading indicators. These variables, especially the financial ones, have performed well over

time and previous research has praised their predictive power.


The results for the predictive power of the CLI and its components for the other key

macroeconomic variables included in this study are less powerful, especially when one considers

the marginal predictive power results. While this may be less than desirable, the intention of

these leading indicators is to predict movements in the aggregate economy and therefore their

predictive power of other variables may be lacking. These indicators should not be the ones of

choice if the goal is to predict future levels of other key macroeconomic variables.

While this study does find significant overall explanatory power of real GDP from nearly

every component in the index over the past 40 years, revised, up to date data has been used, a

luxury forecasters do not have. Many of the papers highlighted in Section II that questioned the

power of the leading index have used unrevised data. The contradiction in results adds support

to the need for more timely data for the index to be used as an accurate predictive tool. The main

conclusion from Steckler (2003) was that revised data offered substantial improvements to the

CLI, however without this he seriously questioned the index’s worth. Additionally, McGuckin,

Ozyildirim, and Zarnowitz (2001) offer a compelling approach to improve the timeliness of data;

one which deserves the attention of those in the business of compiling such indices. As this

study has shown that many components of the CLI offer statistically significant overall

predictive power of output and half of these also provide significant marginal predictive power,

the CLI should continue to be used as a valuable predictive tool.


References:

Enders, Walter (2004), Applied Econometrics Time Series, Hoboken: Wiley. Dueker Michael J. (2002), “Regime-Dependent Recession Forecasts and the 2001 Recession,”

Review, Federal Reserve Bank of St. Louis, November/December. Filardo, Andrew J. (2004) “The 2001 U.S. Recession: What Did Recession Prediction Models

Tell Us?” BIS Working Papers No 148, Monetary and Economic Department. Hamilton, James D. and Gabriel Perez-Quiros (1996), “What Do the Leading Indicators Lead?”

The Journal of Business, 69(1). Jagric, Timotej (2003), “Forecasting with Leading Economic Indicators – A Neural Network

Approach,” Business Economics, National Association for Business Economics, October. Kliesen, Kevin L (2003), “The 2001 Recession: How Was It Different and What Developments

May Have Caused It?” Review, Federal Reserve Bank of St. Louis, September/October. McGuckin, Robert H., Ataman Ozyildrim and Victor Zarnowitz (2001). “The Composite of

Leading Economic Indicators: How to Make It More Timely,” NBER Working Paper No. 8430.

Moore, Geoffrey H. (1979), “The Forty-Second Anniversary of Leading Indicators,”

Contemporary Economic Problems edited by William Fellner. Washington, DC: American Enterprise Institute.

Stekler, H.O. “Interpreting Movements in the Composite Index of Leading Indicators,” Business

Economics, National Association for Business Economics, July. Stock, James H. and Mark W. Watson (1989), “New Indexes of Coincident and Leading

Economic Indicators,” NBER Macroeconomics Annual 1989, edited by O.J. Blanchard and S. Fischer. Cambridge: MIT Press, 352-94.

Stock, James H. and Mark W. Watson (2002), “Has the Business Cycle Changed and Why?”

NBER Macroeconomics Annual 2002, edited by Mark Gertler and Kenneth Rogoff. Cambridge: MIT Press, 159-218.

Stock, James and Mark Watson (2003), “How Did Leading Indicator Forecasts Perform During

the 2001 Recession?” Economic Quarterly, Federal Reserve Bank of Richmond. Volume 89/3.

Tsay, Ruey S. and Chung-Shu Wu (2003), “Forecasting with Leading Indicators Revisited,”

Journal of Forecasting, 22.


Zarnowitz, Victor (1992), “Business Cycles: Theory, History, Indicators, and Forecasting,” NBER Studies in Business Cycles, Volume 27. Chicago: University of Chicago Press, 357-381. [Reprinted from Zarnowitz and Braun 1990]

Zarnowitz, Victor (1998), “Has the Business Cycle Been Abolished?” NBER Working Paper

No. 6367. Zarnowitz, Victor (1999), “Theory and History Behind Business Cycles: Are the 1990s the

Onset of a Golden Age?” Journal of Economic Perspectives, 12(2).

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An Examination into the Predictive Content of the Composite Index of Leading Indicators

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