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

Author

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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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    File URL: http://web2.uconn.edu/economics/working/1995-05.pdf
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    References listed on IDEAS

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    1. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
    2. Sims, Christopher A., 1988. "Bayesian skepticism on unit root econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 463-474.
    3. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
    4. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    7. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    8. Cullity, John P., 1993. "Monitoring business conditions at the CIBCR," International Journal of Forecasting, Elsevier, vol. 9(1), pages 39-48, April.
    9. Zarnowitz, Victor & Moore, Geoffrey H, 1982. "Sequential Signals of Recession and Recovery," The Journal of Business, University of Chicago Press, vol. 55(1), pages 57-85, January.
    10. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, January.
    11. 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.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.

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