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. © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

The views expressed here are those of the author and do not necessarily represent those of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. I thank James Stock and Mark Watson for supplying their original GAUSS program to estimate earlier versions of state index models and for suggesting the application of their national model to regions and states. I thank Keith Sill for assistance in applying that program to earlier state models. Alan Clayton-Matthews provided invaluable advice and a C++ program to estimate the current state models. Finally, I thank Jason Novak for excellent research assistance on this project. Any errors are mine alone.

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Policymakers tend to focus on core inflation measures because they are thought to be better predictors of total inflation over time horizons of import to policymakers. We find little support for this assumption. While some measures of core inflation are less volatile than total inflation, core inflation is not necessarily the best predictor of total inflation. The relative forecasting performance of models using core inflation and those using only total inflation depends on the inflation measure and time horizon of the forecast. Unlike previous studies, we provide a measure of the statistical significance of the difference in forecast errors.

In this paper, we develop and estimate a model that decomposes the variance in office vacancy rates into market-specific, time-specific, and random components. The results indicate significant differences in natural vacancy rates across markets. We also find some persistence in deviations from these natural vacancy rates. The analysis is applied to both central business district (CBD) and suburban office markets. We find that natural vacancy rates differ across CBD markets and across suburban markets. Further, the persistence of disequilibrium in one CBD market seems to differ significantly from that in another. This is not shown to be true for suburban markets. Copyright American Real Estate and Urban Economics Association.