The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On the Failure of the Bootstrap for Matching Estimators
Open accessAbstract
Matching estimators are widely used in empirical economics for the evaluation of programs or treatments. Researchers using matching methods often apply the bootstrap to calculate the standard errors. However, no formal justification has been provided for the use of the bootstrap in this setting. In this article, we show that the standard bootstrap is, in general, not valid for matching estimators, even in the simple case with a single continuous covariate where the estimator is root-N consistent and asymptotically normally distributed with zero asymptotic bias. Valid inferential methods in this setting are the analytic asymptotic variance estimator of Abadie and Imbens (2006a) as well as certain modifications of the standard bootstrap, like the subsampling methods in Politis and Romano (1994).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaart, A. W. V. D. (1998) · doi:10.1017/cbo9780511802256 · Cited by 4,159
Does matching overcome lalonde’s critique of nonexperimental estimators?
A. Smith, J., & E. Todd, P. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.04.011 · Cited by 3,176
Cited by
SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Caliendo, M., & Kopeinig, S. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00527.x · Cited by 6,238
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
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
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2014) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12027 · Cited by 1,315
A Systematic Review of Propensity Score Methods in the Social Sciences
Thoemmes, F. J., & Kim, E. S. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.540475 · Cited by 577
Austin, P. C. (2016) · Statistics in Medicine · doi:10.1002/sim.7084 · Cited by 542
Matching Methods in Practice: Three Examples
Imbens, G. W. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.373 · Cited by 445
Dechezleprêtre, A., Nachtigall, D., & Venmans, F. (2023) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2022.102758 · Cited by 416
LONG-RUN EFFECTS OF PUBLIC SECTOR SPONSORED TRAINING IN WEST GERMANY
Lechner, M., Miquel, R., & Wunsch, C. (2011) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2011.01029.x · Cited by 371
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023) · American Journal of Political Science · doi:10.1111/ajps.12685 · Cited by 349
Causal inference with observational data
Nichols, A. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700403 · Cited by 339
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019) · Marketing Science · doi:10.1287/mksc.2018.1135 · Cited by 287
Robust post-matching inference
Abadie, A., & Spiess, J. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1840383 · Cited by 169
Self-citationKleemann, L., & Abdulai, A. (2013) · Ecological Economics · doi:10.1016/j.ecolecon.2013.06.017 · Cited by 81
The Effectiveness of Private Voucher Education: Evidence From Structural School Switches
Lara, B., Mizala, A., & Repetto, A. (2011) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373711402990 · Cited by 72
Metadata
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2008
- Volume
- 76
- Issue
- 6
- Pages
- 1537-1557
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2156839371
- Cited by
- 798
- References
- 2
- FWCI
- 41.29
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 2800 d)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
