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Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes

  • Mehmet Balcilar

    (Eastern Mediterranean University)

  • Rangan Gupta

    (University of Pretoria)

  • Anandamayee Majumdar

    (Arizona State University)

  • Stephen M. Miller

    (University of Connecticut and University of Nevada, Las Vegas)

This paper provides out-of-sample forecasts of Nevada gross gaming revenue and taxable sales using a battery of linear and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector autoregressive and vector error-correction models with and without Bayesian priors. The non-linear models include non-parametric and semi-parametric models, smooth transition autoregressive models and artificial neural network autoregressive models. In addition to gross gaming revenue and taxable sales, we employ recently constructed coincident and leading employment indexes for Nevada's economy. We conclude that non-linear models generally outperform linear models in forecasting future movements in gross gaming revenue and taxable sales.

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2010-21.

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Length: 47 pages
Date of creation: Jul 2010
Date of revision:
Publication status: Published in Empirical Economics
Handle: RePEc:uct:uconnp:2010-21
Contact details of provider: Postal: University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063
Phone: (860) 486-4889
Fax: (860) 486-4463
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