 On “Imputation of Counterfactual Outcomes when the Errors are Predictable'': Discussions on Misspecification and Suggestions of Sensitivity AnalysesLuis A. F. Alvarez () and Bruno Ferman No 2024_16, Working Papers, Department of Economics from University of São Paulo (FEA-USP) Abstract: Gonçalves and Ng (2024) propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of the errors is correctly specified. In this comment, we first discuss how the assumptions imposed on the errors depend on the model and estimator adopted. This enables researchers to assess the validity of the assumptions imposed on the structure of the errors, and the relevant information set for conditional analyses. We then propose a simple sensitivity analysis in order to quantify the amount of misspecification on the dependence structure of the errors required for the conclusions of conditional analyses to be changed. Keywords: treatment effect; synthetic control; sensitivity analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spa:wpaper:2024wpecon16 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in Working Papers, Department of Economics from University of São Paulo (FEA-USP) Contact information at EDIRC. |
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