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Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

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  • Pami Dua

    (University of Connecticut)

  • Stephen Miller

    (University of Connecticut)

Abstract

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

Suggested Citation

  • 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.
    • Handle: RePEc:uct:uconnp:1995-05
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    References listed on IDEAS

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    Cited by:

    1. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. 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.
    3. Pami Dua & Anirvan Banerji & Stephen M. Miller, 2006. "Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 415-437.
    4. Ahking, Francis, W. & Pattanapancha, Maneechit, 2000. "The Linkage Between State and National Output: A Case Study of Connecticut," The Review of Regional Studies, Southern Regional Science Association, vol. 30(2), pages 137-145, Fall.
    5. Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.
    6. Simonetta Longhi & Peter Nijkamp, 2005. "Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation," Tinbergen Institute Discussion Papers 05-041/3, Tinbergen Institute.

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