Econometric Forecasting of Tourist Arrivals Using Bayesian Structural Time-Series*

IF 0.9 Q3 ECONOMICS

Economic Papers Pub Date : 2023-04-04 DOI:10.1111/1759-3441.12383

Antony Andrews, Sean Kimpton

引用次数: 0

Abstract

This article introduces the Bayesian structural time series (BSTS) as a potential tool for forecasting in the tourism literature. Using data on Australian tourist arrivals in New Zealand, the forecasting accuracy of the estimated model is evaluated using a fixed partitioning approach. The MAPE of the fitted model is 3.11 per cent for the validation stage and 2.75 per cent for the test stage. The BSTS outperforms two other competing models both in the validation and test stage. In addition to forecasting, BSTS also estimates the trend, trend slope, and seasonality that change over time.

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基于贝叶斯结构时间序列的游客到达量经济预测*

本文介绍了贝叶斯结构时间序列（BSTS）作为旅游文献中一种潜在的预测工具。利用澳大利亚游客抵达新西兰的数据，使用固定划分方法评估了估计模型的预测准确性。拟合模型的MAPE在验证阶段为3.11%，在测试阶段为2.75%。BSTS在验证和测试阶段都优于其他两个竞争模型。除了预测，BSTS还估计随时间变化的趋势、趋势斜率和季节性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Economic Papers ECONOMICS-

CiteScore

2.30

自引率

0.00%

发文量

期刊介绍： Economic Papers is one of two journals published by the Economics Society of Australia. The journal features a balance of high quality research in applied economics and economic policy analysis which distinguishes it from other Australian journals. The intended audience is the broad range of economists working in business, government and academic communities within Australia and internationally who are interested in economic issues related to Australia and the Asia-Pacific region. Contributions are sought from economists working in these areas and should be written to be accessible to a wide section of our readership. All contributions are refereed.