Version 1
: Received: 6 September 2019 / Approved: 9 September 2019 / Online: 9 September 2019 (12:11:03 CEST)
How to cite:
Jackson, E.A.; Tamuke, E. Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model. Preprints2019, 2019090102. https://doi.org/10.20944/preprints201909.0102.v1
Jackson, E.A.; Tamuke, E. Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model. Preprints 2019, 2019090102. https://doi.org/10.20944/preprints201909.0102.v1
Jackson, E.A.; Tamuke, E. Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model. Preprints2019, 2019090102. https://doi.org/10.20944/preprints201909.0102.v1
APA Style
Jackson, E.A., & Tamuke, E. (2019). Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model. Preprints. https://doi.org/10.20944/preprints201909.0102.v1
Chicago/Turabian Style
Jackson, E.A. and Edmund Tamuke. 2019 "Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model" Preprints. https://doi.org/10.20944/preprints201909.0102.v1
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 out-of-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.
Keywords
ARIMA Methodology; Out-of-Sample Forecast; Tourist Arrivals; Sierra Leone
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
