Propensity Score Matching in Accounting Research
By Jonathan E. Shipman, Quinn T. Swanquist, Robert L. Whited. 2017
1,313 citations recorded by the source corpus.
Subfields: Accounting, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/dc0f9f023e8db50ab2a799154ae7c8e38d27deca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Özde Öztekin
- H. Al‐Shaer
- Gönül Ҫolak
- K. Ramanna
- Jonas Heese
- Wuchun Chi
- Mozaffar Khan
- Valeri V. Nikolaev
In Statistics and Probability
Citations in the corpus
- The impact of COVID-19 pandemic on bank lending around the world (2021, positive): “According to Shipman et al. (2017), propensity score matching (PSM)”
- The impact of internal audit attributes on the effectiveness of internal control over operations and compliance (2019, methodology): “Specifically, we create an indicator variable for each IAF attribute, which is then used to assign an observation to either a treatment or control group before matching (Shipman et al., 2017).”
- Sustainability reporting quality and post‐audit financial reporting quality: Empirical evidence from the UK (2020, methodology): “I then estimate the propensity score and match it for each year – industry group using a 1% radius matching approach (Shipman, Swanquist, & Whited, 2017).”
- Big N Auditors and Audit Quality: New Evidence from Quasi-Experiments (2019, neutral): “This matching method is also useful to address function form misspecification (Shipman et al. 2016) when simply controlling for firm size in a linear specification might not be sufficient (Lawrence et al.”
- Is the SEC captured? Evidence from comment-letter reviews (2017, background): “replacement in the selection of matches to ensure that we find a meaningful match for each of the PC firms (Shipman et al. 2017).”
- Proxies and Databases in Financial Misconduct Research (2017, neutral): “(2010) and Yu and Yu (2011). Using an AAER-based sample, our SEC-based definition of fraud yields a larger sample than that used by Brazel, Jones, and Zimbelman (2009) and Caskey and Hanlon (2013).”