The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
The Nonparametric Identification of Treatment Effects in Duration Models
Abstract
This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define “no anticipation of treatment” and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single-spell and multiple-spell data. The timing of events conveys useful information on the treatment effect.
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“This is important for identification of τ 2 , since otherwise the changes in the outcome for the treated group between period 1 and 2 could reflect not just the causal effect in period t ' 2 but also the anticipatory effect in period t ' 1 (Abbring and van den Berg, 2003; Malani and Reif, 2015).”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Abbring and van den Berg, 2003
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Difference-in-Differences with multiple time periods
Callaway, B., & Sant’Anna, P. H. C. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.12.001 · Cited by 9,126
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926
Revisiting Event-Study Designs: Robust and Efficient Estimation
Borusyak, K., Jaravel, X., & Spiess, J. (2024) · Review of Economic Studies · doi:10.1093/restud/rdae007 · Cited by 2,280
What's trending in difference-in-differences? A synthesis of the recent econometrics literature
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.03.008 · Cited by 2,027
“This is important for identification of τ 2 , since otherwise the changes in the outcome for the treated group between period 1 and 2 could reflect not just the causal effect in period t = 2 but also the anticipatory effect in period t = 1 (Abbring and van den Berg, 2003; Malani and Reif, 2015).”
Design-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917
Metadata
- Authors
- Abbring, J. H., & van den Berg, G. J.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- ORCID
- Abbring, Jaap H.
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2003-09
- Volume
- 71
- Issue
- 5
- Pages
- 1491-1517
- ISSN
- 0012-9682 (ISSN-L)
- 1468-0262
- OpenAlex
- W2069757101
- Cited by
- 616
- References
- 53
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 18 · 2023: 27 · 2022: 24 · 2021: 18 · 2020: 23 · 2019: 17 · 2018: 23 · 2017: 33 · 2016: 30 · 2015: 25 · 2014: 24 · 2013: 36 · 2012: 31
- FWCI
- 20.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Econometrica (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- treatment effects, duration models, nonparametric identification, causal inference, unobserved heterogeneity, event timing, treatment initiation
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