The improvement of forecasting accuracy Egyptian tourism demand using combining statistical and judgmental forecasts

Abstract EN

study aims to examine methods for improving the accuracy of experts' group-based judgmental adjustments to econometric forecasts of tourism demand of the forecasts we evaluate how accurately annual tourism arrival data in Egypt can be forecast using a combined method.

Over the forecasting period of 2018 -2022, the combined forecasts outperform the baseline forecasts produced by Autoregressive Distributed Lag (ARDL- ECM) models, demonstrating the value of implementing this integration.

Various error measures (APE), (MAPE), and (RMSPE) are used to determine forecasting effectiveness, and statistical tests were performed to evaluate forecast accuracy using the Delphi method and a range of expert judgment adjustments to integrate statistical forecasts and expert judgments.

Several forecasting models are compared to evaluate the performance of the combined method by examining the statistical and judgmentally adjusted forecasts with regression analysis.

Based on the hypothesis tests, it was concluded that the Delphi panel adjustments increased forecast accuracy.

There are several advantages to integrating expert judgments into statistical forecasts.