The U.S. aggregate business cycle is often characterized as a series of distinct recession and expansion phases. We apply a regime-switching model to state-level coincident indices to characterize state business cycles in this way. We find that states differ a great deal in the levels of growth that they experience in the two phases: Recession growth rates are related to industry mix, whereas expansion growth rates are related to education and age composition. Further, states differ significantly in the timing of switches between regimes, indicating large differences in the extent to which state business cycle phases are in concord with those of the aggregate economy.

Since the 1950s the Bureau of Economic Analysis (BEA) has grouped the states into eight regions based primarily on cross-sectional similarities in their socioeconomic characteristics. This paper groups states into regions based on the similarities in their business cycles. We applied k -means cluster analysis to the cyclical components of Stock-Watson-type indices estimated at the state level to group the 48 contiguous states into eight regions with similar cycles. We then compare the cohesion of the regions so defined with the cohesion of the BEA regions. Finally, we examine how that definition affects the results of some recent regional business cycle analysis.

This paper evaluates the use of measures of regional economic activity to forecast tax revenues for New York State and New York City at 3-, 6-, and 12-month horizons. We construct sales- and withholding-tax base series and then apply the methodology of Stock and Watson (1989, 1991) to estimate regional indexes of coincident economic indicators. Employing an out-of-sample forecasting framework, we find that the use of the coincident indexes leads to statistically and economically significant improvements in tax base forecasts compared to those generated from univariate autoregressions. In addition, the coincident indexes produce forecasts that are generally more accurate than forecasts that rely on the use of the coincident indicators separately. Though our analysis focuses on forecasting movements in tax revenue at the state or local level, it is also intended to draw attention to the value the indexes may provide in other applications.

In the late 1980s James Stock and Mark Watson developed for the U.S. economy an alternative coincident index to the one now published by the Conference Board. They used the Kalman filter to estimate a latent dynamic factor for the national economy and designated the common factor as the coincident index. This paper uses the Stock-Watson methodology to estimate a consistent set of coincident indexes for the 50 states. These indexes provide researchers with a comprehensive monthly measure of economic activity that can be used to examine a number of state and regional issues.