Academic journal article NBER Reporter

Indexes of Coincident and Leading Economic Indicators

Academic journal article NBER Reporter

Indexes of Coincident and Leading Economic Indicators

Article excerpt

Indexes of Coincident and Leading Economic Indicators(1)

For 50 years, economists in business and government have used the system of leading economic indicators to gauge the future course of economic activity. The system of leading, coincident, and lagging economic indicators originally was developed by Arthur F. Burns, Wesley C. Mitchell, and their colleagues at the NBER and is currently maintained by the U.S. Department of Commerce (DOC). Some 32 countries throughout the world now have a system of indicators that they use. The indexes of coincident and leading economic indicators themselves--weighted averages of key coincident and leading time series--play a central role in contemporary uses of this system. The coincident index measures the current state of the economy. The leading index often is interpreted as giving advance information about the future direction of the economy, particularly whether toward an expansion or a recession.

In recent work, we have taken a new look at the construction and interpretation of the indexes of coincident and leading economic indicators. The methods used to construct these indexes have remained largely unchanged for the last 30 years. We have exploited recent developments in time-series econometrics to improve the performance of the coincident and leading economic indexes constructed using traditional techniques. This work has resulted in the development of three experimental indexes: an index of coincident economic indicators (CEI), an index of leading economic indicators (LEI), and a new series that we call a "recession index" (RI). These three indexes, their construction, and their interpretation are described in this Research Summary.

The Index of Coincident Economic Indicators

In constructing an index of leading indicators, the first step is to define what this index leads. The index of coincident indicators currently maintained by the DOC is a weighted average of four broad measures of economic activity: industrial production, real personal income less transfer payments, real manufacturing and (1)This report draws on research reported in J.H. Stock and M.W. Watson, "New Indexes of Coincident and Leading Economic Indicators," presented at the NBER Macroeconomics Conference, 1989. This work was funded in part by the NBER. The results of this work are still experimental and do not constitute an official new set of NBER indexes. trade sales, and the number of nonagricultural employees. While each of the series exhibits its own idiosyncratic movements (which include errors of measurement), the common movement among the series may arise from general swings in economic activity, that is, from the business cycle. Thus averaging these series provides one way to eliminate the idiosyncratic movements and obtain a better estimate of swings in overall activity.

But how can this averaging best be done? The traditional NBER/DOC approach is to take a weighted average of contemporaneous growth rates of the coincident series, in which the weights depend on the standard deviations of the series. Although we chose to construct the weights somewhat differently--using an explicit statistical model--the net result is very similar to the DOC coincident index. (Our weights are from an estimated "dynamic factor model," in which the unobserved state of the economy is the sole source of comovements among the coincident variables.)(2) The major difference between the variables in our experimental index and the DOC index is that we use employee-hours rather than the number of employees. (2)In theory the traditional method and the "dynamic factor model" approach could have produced quite different indexes. The fact that the indexes are so similar can be interpreted as providing a formal statistical rationalization for the traditional procedure. The application of dynamic factor models to macroeconomic time-series variables was developed by T. …

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