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The Time-Series Relatedness of State and National Indexes of Leading Indicators and Implications for Regional Forecasting

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  • Gary L. Shoesmith

    (Babcock Graduate School of Management, Wake Forest University, Winston-Salem, NC, gary.shoesmith@mba.wfu.edu)

Abstract

This study investigates the time-series relatedness of state and national indexes of leading indicators and the implications of these results regarding state employment forecasting. Composite indexes of leading indicators are constructed for the United States and each of the fifty states based on housing permits, initial claims for state unemployment compensation, and average weekly hours in manufacturing. The three-component U.S. index ( USLI3 ) is shown to reflect much of the cyclical variation in the U.S. composite of eleven leading indicators ( USLI11 ). Final prediction error (FPE) causality tests and cointegration/long-memory components statistics show that USLI3 generally causes or “drives†the state indexes. These results suggest that national leading indicators may be more useful than similar state indicators in predicting state-level activity. Experimental forecasts confirm that both USLI3 and USLI11 are generally more effective in improving forecasts of state nonfarm employment than the respective fifty state composite indexes.

Suggested Citation

  • Gary L. Shoesmith, 2000. "The Time-Series Relatedness of State and National Indexes of Leading Indicators and Implications for Regional Forecasting," International Regional Science Review, , vol. 23(3), pages 281-299, July.
    • Handle: RePEc:sae:inrsre:v:23:y:2000:i:3:p:281-299
    DOI: 10.1177/016001700761012792
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    References listed on IDEAS

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    1. Pami Dua & Stephen Miller, 1995. "Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut," Working papers 1995-05, University of Connecticut, Department of Economics.
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    5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    6. Dua, Pami & Miller, Stephen M, 1996. "Forecasting Connecticut Home Sales in a BVAR Framework Using Coincident and Leading Indexes," The Journal of Real Estate Finance and Economics, Springer, vol. 13(3), pages 219-235, November.
    7. Hsiao, Cheng, 1981. "Autoregressive modelling and money-income causality detection," Journal of Monetary Economics, Elsevier, vol. 7(1), pages 85-106.
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    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.

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