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

  • Mehmet Balcilar


    (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta


    (Department of Economics, University of Pretoria)

  • Anandamayee Majumdar


    (Department of Mathematics and Statistics, Arizona State University)

  • Stephen M. Miller


    (Department of Economics, 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 Nevada, Las Vegas , Department of Economics in its series Working Papers with number 1103.

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Length: 46 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:nlv:wpaper:1103
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