Examining the hotel room supply and demand in Las Vegas: A simultaneous equations model

Tsai, Henry; Kang, Bomi; Yeh, Ronnie; Suh, Eunju

doi:10.1016/j.ijhm.2005.01.004

Cited by 25 publications

(29 citation statements)

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“…Most notably, price elasticities have been estimated for air transport (Brons, Pels, Nijkamp, & Rietveld, 2002;Gillen, 2004), hotel room demand (Tsai, Kang, Yeh, & Suh, 2006;Walsh, Enz, & Canina, 2004), and visitation of National Parks (e.g. Greiner & Rolfe, 2004).…”

Section: Introductionmentioning

confidence: 99%

Demand elasticity estimates for New Zealand tourism

Schiff¹,

Becken

2011

Tourism Management

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Section: Introductionmentioning

confidence: 99%

Demand elasticity estimates for New Zealand tourism

Schiff¹,

Becken

2011

Tourism Management

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“…According to Qu, Xu, and Tan (2002), hotel room price has a significant effect on the demand for rooms. Tsai, Kang, Yeh, and Suh (2005) further found that the hotel room demand is positively related to the consumer price index (CPI). That is, hotel room price possesses a relative quality, compared to general goods and services, which may either stimulate or deaden the hotel room demand.…”

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confidence: 96%

Tourism and Hotel Competitiveness Research

Tsai

Song

Wong

2009

Journal of Travel & Tourism Marketing

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ABSTRACT. Competitiveness has been a subject of study in the manufacturing and related sectors since the early 1990s. However, only recently have some researchers started to examine the tourism and hospitality competitiveness, both conceptually and empirically, with a particular focus on tourism destinations and the hotel industry. The goal of this article is to review the published studies on destination and hotel competitiveness, provide critiques, and point out future directions in tourism and hotel competitiveness research. Such a review shall provide researchers with a good understanding of the current status of competitiveness research and with a vision for advancing the existing knowledge of destination and hotel competitiveness.

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confidence: 99%

The impact of infectious diseases on hotel occupancy rate based on independent component analysis

Law

Jiang

2010

International Journal of Hospitality Management

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The rapid growth of the tourism and hospitality industries serves as the major driving force for academic researchers to investigate the accurate forecasting of lodging demand in tourist destinations. For instance, Tsai et al. (2006) studied the relationship between hotel room demand/supply functions and room rate using a simultaneous equations model for determinants of room demand/supply in Las Vegas. In terms of optimal hotel rates, Pan (2007) proposed an optimal room rate model to set pricing strategies at peak or low seasons. Recently, Slattery (2009) suggested employing the Otus theory to study the relationships between economic structure and demand/ supply of hotel rooms.In this paper, a novel time series model is presented which models multivariate time series data. The advantage of this model is that the discovered factors are mutually independent and a univariate time series model can be applied to these factors without considering the factors' interdependence. The next section introduces the methodologies of independent component analysis. On the basis of this model, empirical studies on Hong Kong hotel occupancy rates are analyzed. The most dominant factor of infectious diseases is subsequently detected. At last, managerial implications and concluding remarks are provided. The modeling processIndependent component analysis (ICA) is a statistical technique that aims to capture observed data in terms of a linear combination of underlying latent variables. These latent variables are assumed to be non-Gaussian and mutually independent. A typical ICA model for an N-dimensional multivariate time series {x 1 =(x it , . . ., x Nt ) 0 }: t = 1, . . ., T) iswhere s t is a vector of statistically independent latent variables called independent components (ICs). A is an unknown constant mixing matrix, and T is the number of observations of the time series. The task of ICA is to identify both the ICs and the matrix A. Various algorithms for parameter estimation have been developed for ICA. Among them, a widely used one is the FastICA algorithm, which was proposed by Hyvä rinen et al. (2001).Since traditional ICA algorithms lack the ranking of ICs, Wu et al. (2006) proposed an ordering algorithm for independent components based on the minimizing mean squared errors (MSE) criterion. Based on this ordering method, ICs are sorted in a way such that modeling errors are minimized and dominant ICs are identified. Modifying the approach of Wu et al. (2006), the major steps of modeling hotel occupancy rate using ICA are presented as follows:1. Import the multivariate dataset (X) with number of N time series, each time series has T observations. 2. Use the FastICA algorithm to find the independent components (S) and the mixing matrix (A). 3. Use the independent component (IC) ordering method to order the ICs to find 1st IC, 2nd IC, 3rd IC, and so on. A B S T R A C TThe recent global outbreak of Influenza A (H1N1), or the more commonly known as swine flu, has negatively affected the tourism and hospitality industries in many ...

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Examining the hotel room supply and demand in Las Vegas: A simultaneous equations model

Cited by 25 publications

References 1 publication

Demand elasticity estimates for New Zealand tourism

Demand elasticity estimates for New Zealand tourism

Tourism and Hotel Competitiveness Research

The impact of infectious diseases on hotel occupancy rate based on independent component analysis

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