A Probability Model of the Coincident Economic Indicators

The Index of Coincident Economic Indicators, currently compiled by the U.S. Department of Commerce, is designed to measure the state of overall economic activity. The index is constructed as a weighted average of four key macroeconomic time series, where the weights are obtained using rules that dare to the early days of business cycle analysis. This paper presents an explicit rime series model (formally, a dynamic factor analysis or "single index" model) that implicitly defines a variable that can be thought of as the overall state of the economy. Upon estimating this model using data from 1959-1987, the estimate of this unobserved variable is found to be highly correlated with the official Commerce Department series, particularly over business cycle horizons. Thus this model provides a formal rationalization for the traditional methodology used to develop the Coincident Index. Initial exploratory exercises indicate that traditional leading variables can prove useful in forecasting the short-run growth in this series.

[1]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[2]  J. Stock,et al.  Testing for Common Trends , 1988 .

[3]  B. Friedman.  Monetary Policy Without Quantity Variables , 1988 .

[4]  Paul D. Koch,et al.  An Examination of the Commerce Department Leading-Indicator Approach , 1988 .

[5]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[6]  John L. Kling.  Predicting the Turning Points of Business and Economic Time Series , 1987 .

[7]  R. Engle,et al.  COINTEGRATION AND ERROR CORRECTION: REPRESENTATION , 1987 .

[8]  A. Harvey,et al.  Forecasting and Interpolation Using Vector Autoregressions with Common Trends , 1987 .

[9]  P. Perron,et al.  Trends and random walks in macroeconomic time series : Further evidence from a new approach , 1988 .

[10]  G. H. Moore.  The Forty-second Anniversary of the Leading Indicators , 1983 .

[11]  A. Auerbach,et al.  The Index of Leading Indicators: "Measurement without Theory," Thirty-Five Years Later , 1982 .

[12]  Salih N. Neftiçi.  Optimal prediction of cyclical downturns , 1982 .

[13]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[14]  Geoffrey H. Moore,et al.  Sequential Signals of Recession and Recovery , 1982 .

[15]  A. Auerbach.  The Index of Leading Indicators , 1981 .

[16]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[17]  W. E. Wecker,et al.  Predicting the Turning Points of a Time Series , 1979 .

[18]  Beatrice N. Vaccara,et al.  Forecasting with the Index of Leading Indicators , 1978 .

[19]  Thomas J. Sargent,et al.  Business cycle modeling without pretending to have too much a priori economic theory , 1976 .

[20]  Saul H. Hymans.  On the Use of Leading Indicators to Predict Cyclical Turning Points , 1973 .

[21]  Tjalling C. Koopmans,et al.  Measurement Without Theory , 1947 .

[22]  L. Hurwicz,et al.  Measuring Business Cycles. , 1946 .

[23]  Arthur F. Burns,et al.  Statistical Indicators Of Cyclical Revivals , 1938 .