Prof. YANG2

Econometrics

Why is this method important? 
Econometric modeling is essential for establishing causal relationships, forecasting economic trends, and controlling for confounding variables in complex tourism systems. It allows researchers to move beyond simple correlations to understand the structural mechanisms driving phenomena such as hotel performance, regional growth, and inequality. Advanced econometric techniques, such as panel data models and structural equations, are critical for handling the hierarchical and dynamic nature of tourism data, enabling rigorous testing of economic theories, such as the tourism-led growth hypothesis and the Dutch disease effects.
 
What has our team done so far? 
Our work demonstrates a sophisticated application of diverse econometric techniques to solve specific tourism problems. We have extensively utilized panel data models, specifically fixed-effects models, to control for unit-specific heterogeneity when analyzing the impact of COVID-19 government restrictions and the relationship between crime and hotel performance. To address endogeneity and simultaneity, we have employed Dynamic Stochastic General Equilibrium (DSGE) modeling to structurally investigate how tourism booms impact income and wealth inequality in small open economies. In the hospitality sector, we applied Stochastic Frontier Analysis (SFA) to measure hotel efficiency and productivity spillovers, and utilized the Hausman-Taylor model to estimate time-invariant location advantages. Additionally, we have applied mixed-effect ordered logit models to analyze discrete-choice data on hotel location satisfaction, allowing for the decomposition of variance across different levels of the data.

Recommendation

Book Chapter:

Econometric Analysis in Hospitality and Tourism Management

Related Publications (click for all related publication)

  • Article
  • China
  • Econometrics, Big Data

From words to growth: Unveiling government attention to tourism from natural language processing

Yang, Lisi; Yang, Yang; Huang, Xijia; Yan, Kai

2026

TOURISM MANAGEMENT

10.1016/j.tourman.2025.105264

  • Article
  • US
  • Econometrics, Big Data

Past stays and future standards: How customer-mediated knowledge transfer enhances service quality

Zhang, Xiaowei; Huang, Xingyu; Yang, Yang; Liu, Wei

2026

TOURISM MANAGEMENT

10.1016/j.tourman.2025.105341

  • Article
  • US
  • Econometrics, Big Data

How does the number of attribute ratings affect a product’s overall rating? Evidence from TripAdvisor

Zhang, Ziqiong; Yang, Yang; Wang, Xueyan; Wang, Chuxin; Zhang, Zili

2026

INFORMATION & MANAGEMENT

10.1016/j.im.2025.104275

Related Presentations

  • Keynote
  • 07/21/2024

Review of research in tourism economics

2nd Xishan Economics Forum

Guiyang, China

  • Presentation
  • 06/04/2018

Econometric and big data analytics in tourism research

Workshop in Guilin Tourism University

Guilin, Guangxi, China

  • Invited Talk
  • 03/04/2024

COVID19tourism Index and its application in tourism management

University of Perpignan

Perpignan, France

Presentation file

  • Invited Talk
  • 06/16/2014

Spatial econometrics in tourism and hospitality management

CICtourGUNE research center

San Sebastian, Spain

Related Resources

  • Dataset

COVID19tourism Index

The COVID19tourism index was developed to monitor the pandemic’s multifaceted impact on the global tourism industry. This index comprises five distinct sub-indices designed to track the specific effects of COVID-19 across various aspects of tourism activities. By utilizing this tool, destinations are enabled to assess their recovery status, generate rigorous forecasts, and benchmark their performance against potential competitors. Sub-indices The COVID19tourism index is comprised of five distinct sub-indices. These sub-indices were designed to track the specific effects of the pandemic across different aspects of tourism activities.

Dashboard Utility The index functions as a tool that enables destinations to perform three primary functions:
• Evaluate Recovery: Destinations can use the tool to assess their current recovery status.
• Forecast: The tool allows users to produce rigorous forecasts regarding tourism trends.
• Benchmark: Destinations can use the index to benchmark their performance against potential competitors

Link to the COVID19tourism Index Dashboard

Link to download the data

Resource file

Resource link

  • Tool

Hotel Location Selection and Analysis Tool (HoLSAT)

Hotel Location Selection and Analysis Tool (HoLSAT) is a WebGIS-based spatial decision support system and simulation toolkit designed for hotel location assessment. In your study of Beijing hotels, we programmed a simulation toolkit that allows hotel investors to assess various determinants and calculate the probability of a hotel locating in a specific potential site based on estimated model coefficients. Furthermore, in our research on Los Angeles, you developed a location evaluation tool that utilizes a “fishnet” grid system to predict and simulate location preference scores for any site within the research area. This tool allows stakeholders to conduct scenario analyses, evaluating how different traveler portfolios or changes in location factors (such as new metro lines) would influence guest satisfaction with a hotel’s location. Additionally, our work on Hong Kong hotel preferences advocates for a Web-GIS platform that uses willingness-to-pay (WTP) estimates to demonstrate promising location sites for new market entrants

Prof. Yang Yang (YANG2)

School of Sport, Tourism and Hospitality Management, Temple University

U.S.-Asia Center for Tourism and Hospitality Research

Linkedin Researchgate Google Orcid