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
  • 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

  • Article
  • China
  • Econometrics, Spatial Modeling

Together through thick and thin: How does the survival of neighboring restaurants matter?

Zhou, Bo; Li, Zhao Rui; Yang, Yang

2026

INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT

10.1016/j.ijhm.2025.104461

  • 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

Related Presentations

  • 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

  • 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

Related Resources

  • Dataset

Government Attention to Tourism Data

This web-based application is designed to provide researchers and policymakers with an interactive platform for querying and analyzing data regarding Government Attention to Tourism (GAT) in China.

Based on the research findings of Yang, Yang, Huang & Yan (2026), this tool visually demonstrates the spatiotemporal relationships and statistical correlations between the GAT index and various tourism economic indicators. This tool supports a bilingual interface in both Chinese and English. You can switch between the two language modes in real-time by clicking the language toggle button (中文/EN) located in the top-right corner of the page.

Key Features:

Multidimensional Data Coverage: Includes GAT indices and 11 key tourism economic indicators at both the provincial and prefectural (city) levels.

Interactive Visualization: Provides trend analysis, spatial distribution maps, and statistical correlation scatter plots.

Data Resources: Supports viewing summaries of raw data and downloading data.

 

本网页应用旨在为研究人员和政策制定者提供关于中国政府旅游关注度 (Government Attention to Tourism, GAT) 的交互式数据查询与分析平台。

该工具基于 Yang, Yang, Huang & Yan (2026) 的研究成果，通过可视化手段展示了 GAT 指数与各类旅游经济指标之间的时空关系和统计关联。本工具支持中英文双语界面。点击页面右上角的语言切换按钮（中文/EN），即可在两种语言模式间实时切换。

核心功能：

  • 多维数据覆盖： 包含省级和地级层面的 GAT 指数及 11 项关键旅游经济指标。
  • 交互式可视化： 提供趋势分析、空间分布地图和统计关联散点图。
  • 数据资源： 支持查看原始数据摘要及下载。

Resource link

  • 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

  • Dataset

Visualization on life happiness and tourism participation of U.S. counties

This web-based application is designed to visualize the longitudinal spatial-temporal relationship between resident life happiness and tourism participation across the United States. It is built upon the empirical research of Yang, Fu, and Lin (2025), which utilized a panel dataset covering over 3,000 U.S. counties to examine how domestic and international tourism activities influence subjective well-being.

Key Features:

  • Spatial Visualization: Displays the geographical distribution of life satisfaction and tourism participation rates on an interactive map using the academic standard Albers USA projection.
  • Correlation Analysis: Dynamically illustrates the statistical relationship between the change in tourism participation and life satisfaction.
  • Distribution Analysis: Provides histograms for all variables to identify data characteristics, skewness, and outliers.
  • Interactive Exploration: Allows users to customize the view by searching for specific counties, toggling years, and adjusting color schemes.

 

Resource link

Prof. Yang Yang (YANG2)

School of Sport, Tourism and Hospitality Management, Temple University

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

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