The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
MODEL SELECTION AND INFERENCE: FACTS AND FICTION
Abstract
Model selection has an important impact on subsequent inference. Ignoring the model selection step leads to invalid inference. We discuss some intricate aspects of data-driven model selection that do not seem to have been widely appreciated in the literature. We debunk some myths about model selection, in particular the myth that consistent model selection has no effect on subsequent inference asymptotically. We also discuss an “impossibility” result regarding the estimation of the finite-sample distribution of post-model-selection estimators.
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
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Cited by
Dziak, J. J., Lanza, S. T., & Tan, X. (2014) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2014.919819 · Cited by 441
Pre-Event Trends in the Panel Event-Study Design
Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019) · American Economic Review · doi:10.1257/aer.20180609 · Cited by 427
Cluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407
A re‐evaluation of fixed effect(s) meta‐analysis
Rice, K., Higgins, J. P. T., & Lumley, T. (2018) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12275 · Cited by 287
Schmidheiny, K., & Siegloch, S. (2023) · Journal of Applied Econometrics · doi:10.1002/jae.2971 · Cited by 293
Metadata
- Authors
- Leeb, H., & Pötscher, B. M.
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 2005-02
- Volume
- 21
- Issue
- 1
- Pages
- 21-59
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W2022943305
- Cited by
- 701
- References
- 1
- FWCI
- 13.42
- Type
- journal article
- Language
- en
- Open access
- closed
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.