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Optimizing control variable selection with algorithms: Parsimony and precision in regression analysis

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  • Fernando Campayo-Sanchez
  • Juan Luis Nicolau

Abstract

This research note explores the pivotal role of control variables in any tourism and hospitality research that utilizes regression models in statistical analyses. While theory-driven independent variables offer insight into expected effects, the inclusion of control variables is crucial for mitigating potential confounding factors. In an attempt to strike a balance between model complexity and parsimony, researchers face the challenge of selecting the optimal control variables. To address this issue, the study tests three alternative methods: genetic algorithms, lasso models, and the branch and bound algorithm. Despite their underutilization in tourism research, these methods offer efficient means of selecting control variables, enhancing model precision and interpretation without unnecessarily convoluting the model with irrelevant factors.

Suggested Citation

  • Fernando Campayo-Sanchez & Juan Luis Nicolau, 2025. "Optimizing control variable selection with algorithms: Parsimony and precision in regression analysis," Tourism Economics, , vol. 31(4), pages 803-810, June.
    • Handle: RePEc:sae:toueco:v:31:y:2025:i:4:p:803-810
    DOI: 10.1177/13548166241287953
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    References listed on IDEAS

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    1. Keling Wang & Yaqiong Miao & Ming-Hsiang Chen & Dengfeng Hu, 2019. "Philanthropic giving, sales growth, and tourism firm performance: An empirical test of a theoretical assumption," Tourism Economics, , vol. 25(6), pages 835-855, September.
    2. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    3. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
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