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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, Famagusta, North Cyprus,via Mersin 10, Turkey)

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

  • Anandamayee Majumdar

    ()

    (School of Mathematical & Statistical Sciences, Arizona State University)

  • Stephen M. Miller

    ()

    (College of Business, University of Las Vegas, Nevada)

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 semiparametric 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 Pretoria, Department of Economics in its series Working Papers with number 201018.

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Length: 46 pages
Date of creation: Jul 2010
Date of revision:
Handle: RePEc:pre:wpaper:201018
Contact details of provider: Postal: PRETORIA, 0002
Phone: (+2712) 420 2413
Fax: (+2712) 362-5207
Web page: http://www.up.ac.za/economics

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  1. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.
  2. Morten O. Ravn & Zacharias Psaradakis & Martin Sola, 2005. "Markov switching causality and the money-output relationship," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 665-683.
  3. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper Series 42_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  4. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
  5. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, vol. 27(i05).
  6. Philip Rothman, . "Forecasting Asymmetric Unemployment Rates," Working Papers 9618, East Carolina University, Department of Economics.
  7. 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 219-35, November.
  8. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
  9. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.
  10. Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
  11. van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Research Papers EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  12. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
  13. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  14. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
  15. 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.
  16. Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," SSE/EFI Working Paper Series in Economics and Finance 561, Stockholm School of Economics, revised 04 Nov 2004.
  17. 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 113-44, January.
  18. 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.
  19. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
  20. Philippe J. Deschamps, 2008. "Comparing smooth transition and Markov switching autoregressive models of US unemployment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 435-462.
  21. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  22. Rothman, Philip & van Dijk, Dick & null, Philip Hans, 2001. "Multivariate Star Analysis Of Money Output Relationship," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 506-532, September.
  23. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
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