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Modelling and Forecasting Demand for Nepali Tourism

Author

Listed:
  • Shoora B. Paudyal, Ph.D.

    (Tribhuvan University, Central Department of Economics)

Abstract

In this paper international demand for Nepali tourism from the selected major markets has been estimated using time series data of number of tourist arrivals, per capita income, own price and prices of related goods. Autoregressive distributive lagged (ARDL) models are applied as a tool of estimation. This study confirms that tourism demand for Nepal is the composite function of disposable income, own price, cross price, lags of these variables, word of mouth of the visitors and qualitative factors captured by dummies. The most important policy implication can be derived from the words of mouth of the visitors. This manifests that only the good impression on the visitors can generates better words of mounth in favour of destination which underscores the up-gradation of the tourist products for the better image of the destination. The best performed models are used for forecasting the growth rates of tourist arrivals from the eight major markets for 2010 to 2020. The forecasted growth rates of tourist arrivals from major eight market are found very close to the actual average annual growth rates for 2006 to 2010.

Suggested Citation

  • Shoora B. Paudyal, Ph.D., 2014. "Modelling and Forecasting Demand for Nepali Tourism," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 26(1), pages 58-89, April.
    • Handle: RePEc:nrb:journl:v:26:y:2014:i:1:p:58
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    References listed on IDEAS

    as
    1. Haiyan Song & Stephen F. Witt & Gang Li, 2003. "Modelling and Forecasting the Demand for Thai Tourism," Tourism Economics, , vol. 9(4), pages 363-387, December.
    2. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Demand; Modelling; ARDL Model; Diagnostic Tests; Restrictive Models; Wickens-Bruesch ECM; Johansen Maximum Likelihood ECM; Forecasts.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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