THE CYCLICAL INDICATOR APPROACH. The composite indexes are the key elements in an analytic system designed to signal peaks and troughs in the business cycle. The leading and coincident indexes are essentially composite averages of between five and eight individual leading or coincident indicators. (See below for details.) They are constructed to summarize and reveal common turning point patterns in economic data in a clearer and more convincing manner than any individual component—primarily because they smooth out some of the volatility of individual components.

Historically, the cyclical turning points in the leading index have occurred before those in aggregate economic activity, while the cyclical turning points in the coincident index have occurred at about the same time as those in aggregate economic activity.

A change in direction in a composite index does not signal a cyclical turning point unless the movement is of significant size, duration, and scope. Historical analysis with U.S. data shows recession warnings are best determined by looking for negative growth of at least 1 percent, coupled with declines in at least half of the components over a six-month span. Further explanations of the cyclical indicator approach and the composite index methodology appear in The Conference Board’s Business Cycle Indicators page:     Methodology and Revisions.

Germany Composite Indexes: Components and Standardization Factors

	Leading Index	Factor
1.	New Orders, Investment Goods	.0604
2.	Applications for Unemployment Compensation	.0287
3.	Yield Spread, 10 Year minus 3 month	.3352
4.	Change in Inventories	.0999
5.	Gross Enterprise and Property Income	.0828
6.	Stock Prices	.0311
7.	New Orders, Residential Construction	.0415
8.	Growth Rate for Consumer Price index for Services	.2079
9.	Consumer Confidence Index	.1125

 

	Coincident Index	Factor
1.	Manufacturing Sales	.0605
2.	Gross Domestic Product	.3519
3.	Industrial Production	.0898
4.	Retail Sales	.0879
5	Unemployment Rate	.4099

Notes:  The component factors are inversely related to the standard deviation of the month-to-month changes in each component. They are used to equalize the volatility of the contribution from each component and are "normalized" to sum to 1. These factors were last revised effective with the June 27, 2000 release, and all historical values for the two composite indexes were revised at that time to reflect the changes. (Under normal circumstances, updates to the leading and coincident indexes only incorporate revisions to data over the past six months.)

The factors above were calculated using 1973-1998 as the sample period for measuring volatility for the leading index, and 1991-1998 as the sample period for the coincident index. There are additional sample periods as the result of different starting dates for the component data. When one or more components is missing, the other factors are adjusted proportionately to ensure that the total continues to sum to 1. For additional information on the standardization factors and the index methodology visit the following web page: Methodology and Revisions.

To address the problem of lags in available data, those leading and coincident indicators that are not available at the time of publication are estimated using statistical imputation. An autoregressive model is used to estimate each component. The resulting indexes are constructed using real and estimated data, and will be revised as the data unavailable at the time of publication become available. Such revisions are part of the monthly data revisions, now a regular part of the U.S. Business Cycle Indicators program. The main advantage of this procedure is to utilize in the leading index the data, such as stock prices, that are available sooner than other data on real aspects of the economy, such as and new orders and changes in inventory. Empirical research by The Conference Board suggests there are real gains in adopting this procedure to make all the indicator series as up-to-date as possible.