Forecasting occupancy rate with Bayesian compression methods

Assaf, A.G. and Tsionas, M.G. (2019) Forecasting occupancy rate with Bayesian compression methods. Annals of Tourism Research, 75. pp. 439-449. ISSN 0160-7383

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The curse of dimensionality is a challenge that researchers often face when dealing with large Vector Autoregressions (VARs). Different approaches have been proposed in the literature to address this issue. In this paper, we propose a new method based on the idea of compressed regression. In particular, we introduce two novel nonlinear compressed VARs to forecast the occupancy rate of hotels that compete within a narrow geographical area. We make the models more flexible through the introduction of neural networks, and compare their performance against several competing models. The empirical results show that the new compressed VARs outperform all other models, and their accuracy is preserved across nearly all forecast horizons from 1 to 36 months.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Tourism Research
Uncontrolled Keywords:
?? bayesiancompression methodshotel occupancy ratelarge vector autoregressions (vars)neural networksdevelopmenttourism, leisure and hospitality management ??
ID Code:
Deposited By:
Deposited On:
12 Feb 2019 15:45
Last Modified:
01 May 2024 00:09