Combined Hierarchical Tourist Arrival Forecasts for Great Zimbabwe National Monuments

Precise tourism estimates for tourism destination sites are crucial for decision-making. The objective of the study is to model and project Great Zimbabwe National Monuments (GZNM) tourist arrivals by combining hierarchical tourism forecasts. The approach improves tourism forecasting accuracy. GZNM monthly tourist arrivals are grouped according to tourism sources. A logarithm transformation is applied to tame the volatile data. Forecasting accuracy of the Simple Average Combination Method (SACM) and three hierarchical forecasting approaches (top-down, bottom-up, and optimal combination) were compared. The SACM under Autoregressive Integrated Moving Average (ARIMA) outperformed the other models, according to Root Mean Square Error (RMSE) measure. SACM is used to combine future tourist arrivals for the following 60 months and show a slow increase in tourist arrivals at GZNM. The data used in modeling are outside the COVID-19 pandemic period. Tourism stakeholders are encouraged to adopt the SACM in future tourism projections as it improves forecasting accuracy. Tourism stakeholders could carefully strategise and plan a recovery and ensure improvement in the tourism sector beyond the COVID-19 pandemic period. The COVID-19 pandemic is significantly affecting the tourism industry, reducing tourist arrivals to zero in some cases. The study revealed a fresh line of inquiry into how combining projections can increase forecasting accuracy © 2022 AJHTL /Author(s)

Modelling Tourist Arrivals in South Africa To Assess The Impact of the COVID-19 Pandemic on the Tourism Sector

The paper examines tourism flows from all foreign countries to South Africa (SA) from 2009 to February 2020 using time series models. The resultant model is used to forecast and assess the impact of COVID-19 on tourist arrivals in South Africa by comparing with actual tourist arrivals after February 2020. Monthly data on tourist arrivals to South Africa from the Overseas, the Southern African Development Commission (SADC), other African countries and those who did not specify area of origin were considered. The Box and Jenkins methodology identified the ARIMA(1,1,0)(1,1,0)12 as the best model as confirmed by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The model has good forecasting power as evidenced by its Mean Absolute Percentage Error (MAPE). Therefore, the fitted model’s forecasts may be used in the absence of COVID-19.The COVID-19 adversely affected forecasting of future values as forecasted values differ greatly with actual observations recorded since COVID-19 induced lockdown measures in March of 2020. This study helps to have an overview of the total contribution being realized from tourism receipts that can be inferred through tourist arrivals before and after the COVID-19 pandemic and how best the tourism sector may be rejuvenated. The study highlights are that the number of tourist arrivals to South Africa follows an ARIMA(1,1,0)(1,1,0)12 model. Prior to the COVID-19 pandemic, there was a persistent upward trend and seasonality in tourist arrivals to South Africa since the 1994 democratic elections. South Africa is currently losing more than 90% of its monthly tourist arrivals because of the pandemic, and shows little sign of an imminent recovery, hence a devastating impact on the tourism industry. © 2022 AJHTL /Author(s)

Modelling International Tourist Arrivals Volatility in Zimbabwe Using a GARCH Process

The aim of the paper was to develop bootstrap prediction intervals for international tourism demand and volatility in Zimbabwe after modelling with an ARMA-GARCH process. ARMA-GARCH models have better forecasting power and are capable of capturing and quantifying volatility. Bootstrap prediction intervals can account for future uncertainty that arises through parameter estimation. The monthly international tourism data obtained from the Zimbabwe Tourism Authority (ZTA) (January 2000 to June 2017) is neither seasonal nor stationary and is made stationery by taking a logarithm transformation. An ARMA(1,1) model fits well to the data;with forecasts indicating a slow increase in international tourist arrivals (outside of the Covid-19 period). The GARCH(1,1) process indicated that unexpected tourism shocks will significantly impact the Zimbabwe international tourist arrivals for longer durations. Volatility bootstrap prediction intervals indicated minimal future uncertainty in international tourist arrivals. For the Zimbabwe tourism industry to remain relevant, new tourism products and attraction centres need to be developed, as well as embarking on effective marketing strategies to lure even more tourists from abroad. This will go a long way in increasing the much-needed foreign currency earnings needed to revive the Zimbabwean economy. © 2020 AJHTL /Author(s) ;Open Access – Online @ www.ajhtl.com

Hierarchical Forecasting of the Zimbabwe International Tourist Arrivals

The objectives of the paper is to: (1) adopt the hierarchical forecasting methods in modelling and forecasting international tourist arrivals in Zimbabwe;and (2) coming up with Zimbabwe international tourist arrivals Prediction Intervals (PIs) in Quantile Regression Averaging (QRA) to hierarchical tourism forecasts. The unavailability of statistical models for Zimbabwe international tourist arrivals that cater for disaggregated tourism data and account for uncertainty due to parameter estimation methods, has resulted in poor marketing strategies, infrastructure and policies targeting wrong tourism groups. Furthermore, the country is failing to attract significant Foreign Direct Investment for particular tourist arrivals. Zimbabwe’s monthly international tourist arrivals data from January 2002 to December 2018 was used. The data set was disaggregated according to the purpose of the visit. Three hierarchical forecasting approaches, namely top-down, bottom-up and optimal combination approaches were applied to the data. The results showed the superiority of the bottom-up approach over both the top-down and optimal combination approaches. Forecasts indicate a general increase in aggregate series. The combined methods provide a new insight into modelling tourist arrivals. The approach is useful to the government, tourism stakeholders, and investors among others, for decision-making, resource mobilisation and allocation. The Zimbabwe Tourism Authority (ZTA) could adopt the forecasting techniques to produce informative and precise tourism forecasts. The data set used is before the COVID-19 pandemic and the models indicate what could happen outside the pandemic. During the pandemic the country was under lockdown with no tourist arrivals to report on. The models are useful for planning purposes beyond the COVID-19 pandemic. Copyright © 2021 International Academic Press