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A Comparison of Different Data-driven Procedures to Determine the Bunching Window

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  • Vincent Dekker
  • Karsten Schweikert

Abstract

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

Suggested Citation

  • Vincent Dekker & Karsten Schweikert, 2021. "A Comparison of Different Data-driven Procedures to Determine the Bunching Window," Public Finance Review, , vol. 49(2), pages 262-293, March.
    • Handle: RePEc:sae:pubfin:v:49:y:2021:i:2:p:262-293
    DOI: 10.1177/1091142121993055
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    Cited by:

    1. Takafumi Suzuki, 2023. "Bunching Estimation and Its Theoretical and Empirical Progress," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 19(4), pages 1-25, October.

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