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Predicting disaggregated tourist arrivals in Sierra Leone using ARIMA model

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  • Jackson, Emerson Abraham
  • Tamuke, Edmund

Abstract

This study have uniquely mad use of Box-Jenkins ARIMA models to address the core of the threes objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research have testify the achievements of this through successful nine months outof-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly outlined possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low level tourist arrival can be managed through collaboration between authorities concerned with the management of tourist arrivals in the country.

Suggested Citation

  • Jackson, Emerson Abraham & Tamuke, Edmund, 2019. "Predicting disaggregated tourist arrivals in Sierra Leone using ARIMA model," MPRA Paper 96845, University Library of Munich, Germany, revised 23 Dec 2019.
    • Handle: RePEc:pra:mprapa:96845
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    References listed on IDEAS

    as
    1. Emerson Abraham Jackson & Edmond Tamuke & Abdulai Sillah, 2018. "Modelling Monthly Headline Consumer Price Index (HCPI) through Seasonal Box-Jenkins Methodology," International Journal of Sciences, Office ijSciences, vol. 7(01), pages 51-56, January.
    2. Edmund TAMUKE & Emerson JACKSON & Abdulai SILLAH, 2018. "Forecasting Inflation In Sierra Leone Using Arima And Arimax: A Comparative Evaluation. Model Building And Analysis Team," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 9(1), pages 63-74.
    3. James W. Taylor, 2008. "A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center," Management Science, INFORMS, vol. 54(2), pages 253-265, February.
    4. repec:srs:journl:jasf:v:9:y:2018:i:1:p:34-44 is not listed on IDEAS
    5. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    6. Ikechukwu Kelikume & Adedoyin Salami, 2014. "Time Series Modeling and Forecasting Information: Evidence from Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(2), pages 41-51.
    7. EMERSON Abraham Jackson, 2018. "Comparison Between Static And Dynamic Forecast In Autoregressive Integrated Moving Average For Seasonally Adjusted Headline Consumer Price Index," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 70(1), pages 53-65, August.
    8. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    9. Emerson JACKSON & Edmund TAMUKE, 2018. "Probability Forecast Using Fan Chart Analysis A Case of the Sierra Leone Economy," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 9(1), pages 34-44.
    10. Jackson, Emerson Abraham & Tamuke, Edmund, 2018. "Probability Forecast Using Fan Chart Analysis: A case of the Sierra Leone Economy," MPRA Paper 88853, University Library of Munich, Germany, revised 04 Sep 2018.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. JACKSON Emerson Abraham & TAMUKE Edmund & JABBIE Mohamed, 2019. "Disaggregated Short-Term Inflation Forecast (Stif) For Monetary Policy Decision In Sierra Leone," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 71(3), pages 31-53, November.

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    More about this item

    Keywords

    ARIMA Methodology; Out-of-Sample Forecast; Tourist Arrivals; Sierra Leone;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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