Prague Economic Papers 2024, 33(3):261-276 | DOI: 10.18267/j.pep.863

Optimization Strategy for the Modeling and Estimation of Interactive Effects

Xiaohui Hu ORCID...
School of Economics, Jiaxing University, Jiaxing, China

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias caused by common LASSO methods. To overcome the three problems simultaneously, post-double selection is used to screen for the interaction terms that need to be included in the model, and the variance estimates are expanded to measure the uncertainty of the interaction effects and marginal effects. Monte Carlo simulations analyze the main factors affecting conditional and non-linear relationships: covariance and sample size. The results of empirical examples show that different model settings and estimation methods can lead to observable differences in the conclusion of treatment effect heterogeneity, and in general, post-double selection has better performance than other estimation methods.

Keywords: interactive effects, model misspecification, regularization bias, post-double selection
JEL classification: C13, C3, C5

Received: November 23, 2023; Revised: April 3, 2024; Accepted: April 22, 2024; Published: June 28, 2024  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Hu, X. (2024). Optimization Strategy for the Modeling and Estimation of Interactive Effects. Prague Economic Papers, 33(3), 261-276. doi: 10.18267/j.pep.863
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Baker, A. C., Larcker D. F., Wang, C. C.Y. (2022). How Much Should We Trust Staggered Difference-in-differences Estimates Journal of Financial Economics, 144(2), 370-395. https://doi.org/10.1016/j.jfineco.2022.01.004 Go to original source...
    2. Beiser-McGrath, J., Beiser-McGrath, L. (2020). Problems with Products? Control Strategies for Models with Interaction and Quadratic Effects. Political Science Research and Methods, 8(4), 707-730. http://doi.org/10.1017/psrm.2020.17 Go to original source...
    3. Belloni, A., Chernozhukov, V., Kato, K. (2014). Uniform Post-selection Inference for Least Absolute Deviation Regression and Other Z-estimation Problems. Biometrika, 102(1), 77-94. http://doi.org/10.1093/biomet/asu056 Go to original source...
    4. Belloni, A., Chernozhukov, V., Hansen, C. (2016). Inference in High-dimensional Panel Models with an Application to Gun Control. Journal of Business & Economic Statistics, 34(4), 590-605. http://doi.org/10.1080/07350015.2015.1102733 Go to original source...
    5. Berry, W. D., DeMeritt, JHR., Esarey, J. (2016). Bias and Overconfidence in Parametric Models of Interactive Processes. American Journal of Political Science, 60(2), 521-539. http://doi.org/10.1111/ajps.12123 Go to original source...
    6. Criscuolo, C., Martin, R., Overman, H. (2019). Some Causal Effects of an Industrial Policy. American Economic Review, 109(1), 48-85. http://doi.org/10.1257/aer.20160034 Go to original source...
    7. Goodman-Bacon, A. (2021). Difference-in-differences with Variation in Treatment Timing. Journal of Econometrics, 225(2), 254-277. https://doi.org/10.1016/j.jeconom.2021.03.014 Go to original source...
    8. Hainmueller, J., Hazlett, C. (2014). Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach. Political Analysis, 22(2), 143-168. http://doi.org/10.1093/pan/mpt019 Go to original source...
    9. Mastrobuoni G, Pinotti, P. (2015). Legal Status and the Criminal Activity of Immigrants. American Economic Journal: Applied Economics, 7(2), 175-206. http://doi.org/10.1257/app.20140039 Go to original source...
    10. Powell, D. (2020). Quantile Treatment Effects in the Presence of Covariates. The Review of Economics and Statistics, 102(5), 994-1005. http://doi.org/10.1162/rest_a_00858 Go to original source...
    11. Qian, X. S., Kang, J., Tang, Y. L (2018). Industrial Policy, Efficiency of Capital Allocation and Firm's Total Factor Productivity - Evidence from a Natural Experiment in China. China Industrial Economics, (8), 42-59. http://doi.org/10.19581/j.cnki.ciejournal.2018.08.003 Go to original source...
    12. Rodrik D. (2012) Why Learn Nothing from Regressing Economic Growth on Policies. Seoul Journal of Economics, 25(2), 137-151.
    13. Sun, X. H., Zhang, J. N., Zheng, H. (2020). Will Replacing BT with VAT Promote the Integrated Development of Manufacturing and Services? China Industrial Economics, (8), 5-23. http://doi.org/10.19581/j.cnki.ciejournal.2020.08.001 Go to original source...
    14. Vansteelandt, S., VanderWeele T. J., Tchetgen, E. J. (2008). Multiply Robust Inference for Statistical Interactions. Journal of the American Statistical Association, 103(484), 1693-1704. http://doi.org/10.1198/016214508000001084 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content