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Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models

Author

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  • Pami Dua

    (Delhi School of Economics, New Delhi, India)

  • Anirvan Banerji

    (Economic Cycle Research Institute, New York, USA)

  • Stephen M. Miller

    (University of Nevada, Las Vegas, Las Vegas, Nevada, USA)

Abstract

The leading and coincident employment indexes for the state of Connecticut developed following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new coincident index shows improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:jof:jforec:v:25:y:2006:i:6:p:415-437
    DOI: 10.1002/for.996
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    Cited by:

    1. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    2. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    3. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
    4. Hong Chen, 2010. "Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms," The Journal of Real Estate Finance and Economics, Springer, vol. 40(3), pages 310-331, April.
    5. Rangan Gupta & Moses M. Sichei, 2006. "A Bvar Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 391-409, September.
    6. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    7. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
    8. Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
    9. repec:emu:wpaper:dp15-01.pdf is not listed on IDEAS
    10. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    11. Shihai Dong & Yandong Wang & Yanyan Gu & Shiwei Shao & Hui Liu & Shanmei Wu & Mengmeng Li, 2020. "Predicting the turning points of housing prices by combining the financial model with genetic algorithm," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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