On the Relationship between Length of Stay and Total Trip Expenditures: A Case Study of Instrumental Variable (IV) Regression Analysis

Thrane, Christer

doi:10.5367/te.2014.0357

Cited by 21 publications

(32 citation statements)

References 19 publications

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“…Interestingly, while in all other models it is significant, when the 2SLS estimation is applied, its size is reduced and its significance disappears. This reduction in size is in line with the results obtained by Thrane (2015), as the other models do not control for endogeneity, and consequently, attribute the whole effect of length of stay on expenses to this variable.…”

Section: Model Estimationsupporting

confidence: 86%

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Determinant Factors of Tourist Expenses

Park

Woo

Nicolau

2019

Journal of Travel Research

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The objective of this article is to analyze travel expenses across and within types. The empirical application examines the determinant factors of total expenses, controlling for potential endogeneity, and relies on quantile regression to analyze the effects of information search behavior on the distribution of total expenses as well as accommodation, shopping, food and beverages, and local transportation expenses. The role of information sources in predicting travel spending behaviors is a new dimension in the literature on expenses, and a sample of 48,113 travelers has led to the detection of effects of variables with relevant managerial implications (e.g., while official information centers show positive impacts at the upper levels of accommodation expenses, they present null effects at the highest levels of shopping expenses) as well as theoretical implications (special attention should be drawn to the variable length of stay, which, after being controlled by endogeneity, completely loses its significance).

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Section: Model Estimationsupporting

confidence: 86%

“…The 2SLS estimation requires the use of instrumental variables. In line with Thrane (2015), the variable length of stay can be instrumented via “number of previous visits to the destination” and “level of satisfaction.”…”

Section: Methodsmentioning

confidence: 99%

Determinant Factors of Tourist Expenses

Park

Woo

Nicolau

2019

Journal of Travel Research

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“…A concern in IV models is the validity of the ''instrument variables''. In simple terms, instruments should be both relevant and exogenous (Stock and Watson, 2007;Thrane, 2015). In other words, instruments should be correlated with the endogenous variable (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…That is to say, the OLS estimate of y on z will be biased in this case. Likewise, it is well recognized that so-called instrumental variable (IV) regression is the preferred remedy for rectifying this (Stock and Watson, 2007; Thrane, 2015).…”

Section: Case Study B: Two Binary Choices Y and Z Where Y Affects Zmentioning

confidence: 99%

Analysing related choices in tourism research

Thrane¹

2015

Tourism Economics

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There is currently no unified framework for analysing related choices in tourism contexts, and this study aims to fill this void. Taking an intuitive approach, the author first shows how a number of previous tourism studies may be thought of as variations of certain simplified choice scenarios. Proceeding with three non-technical case studies, the article demonstrates how models that involve related choice variables should be estimated. In passing, several problems that typically surface in this kind of research are addressed. Many of these problems are not acknowledged in present-day tourism research; more frequently, they are conceptually conceded but not handled satisfactorily from an econometric point of view. Finally, some advice for future research of this nature and some model extensions are provided.

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“…The final analysis in this study examined the influence of DMOs' advertising efforts on individual travelers' behaviors (i.e., expenditure, the length of stay) using seemingly unrelated regression (SUR) to assess the marginal impact of facet-level advertisements response on both overall trip expenditures and the length of stay where the dependent variables were log-transformed and where the moderating variables (i.e., traveler and trip characteristics) were included in the model to exclude the confounding effects. In this study, overall trip expenditure and the length of stay are assumed to be influenced by a set of travelers and trip characteristics and advertising responses simultaneously [57,58]. As such, two separate ordinary least squares (OLS) regression analyses-i.e., for both overall trip expenditure and the length of stay-would provide invalid parameter estimates due to the endogeneity problems and a potential correlation between the error terms in two separate equations.…”

Section: Individual-level Analysismentioning

confidence: 99%

Differences in Tourist Behaviors across the Seasons: The Case of Northern Indiana

2019

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Seasonality is an essential factor influencing tourism demand and traveler behavior at the destination. As such, seasonality (i.e., the influx of tourists) needs to be managed by destination marketing organizations. Most tourism studies have focused mainly on the forecasting methods/metrics and the effect of seasonality at the aggregate level rather than understanding seasonal differences in the nature of the traveler and travel experience. The purpose of this study is to understand seasonality at both the aggregate market level and individual traveler level. As such, this study first utilizes the concept of the gravity model to understand seasonality in the number of inquiries through an official website. This study, then, uses seemingly unrelated regressions to estimate simultaneously the effect of various trip-related factors on overall trip expenditures and the length of the trip. The results show that the impact of seasonality on aggregated demand is surprisingly consistent across the seasons; however, individual-level analyses indicate that traveler behavior and travelers’ responses to advertising differ significantly across seasons. Thus, destination marketers need to understand the nature of seasonality of their specific markets more accurately to provide appropriate tourism products/services to their current and potential travelers.

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On the Relationship between Length of Stay and Total Trip Expenditures: A Case Study of Instrumental Variable (IV) Regression Analysis

Cited by 21 publications

References 19 publications

Determinant Factors of Tourist Expenses

Determinant Factors of Tourist Expenses

Analysing related choices in tourism research

Differences in Tourist Behaviors across the Seasons: The Case of Northern Indiana

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