Arbeitspapier
Cluster sample inference using sensitivity analysis: The case with few groups
This paper re-examines inference for cluster samples. Sensitivity analysis is proposed as a new method to perform inference when the number of groups is small. Based on estimations using disaggregated data, the sensitivity of the standard errors with respect to the variance of the cluster effects can be examined in order to distinguish a causal effect from random shocks. The method even handles just-identified models. One important example of a just-identified model is the two groups and two time periods difference-indifferencessetting. The method allows for different types of correlation over time and between groups in the cluster effects.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 2009:15
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Thema
-
Cluster-correlation
Difference-in-Differences
Sensitivity analysis
Clusteranalyse
Sensitivitätsanalyse
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Vikström, Johan
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for Labour Market Policy Evaluation (IFAU)
- (wo)
-
Uppsala
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Vikström, Johan
- Institute for Labour Market Policy Evaluation (IFAU)
Entstanden
- 2009
Ähnliche Objekte (12)
Large-sample inference on spatial dependence
Difference-in-differences inference with few treated clusters
Wild Bootstrap Randomization Inference for Few Treated Clusters
Finite sample inference for the maximum score estimand
Credible Granger-causality inference with modest sample lengths: A cross-sample validation approach
Randomization inference for difference-in-differences with few treated clusters
How cluster-robust inference is changing applied econometrics
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Cluster-robust inference: A guide to empirical practice
Small sample performance of indirect inference on DSGE models
Indirect inference and small sample bias - Some recent results
Sensitivity of sample for simulation-based reliability analysis methods
Large-sample inference on spatial dependence
Difference-in-differences inference with few treated clusters
Wild Bootstrap Randomization Inference for Few Treated Clusters
Finite sample inference for the maximum score estimand
Credible Granger-causality inference with modest sample lengths: A cross-sample validation approach
Randomization inference for difference-in-differences with few treated clusters
How cluster-robust inference is changing applied econometrics
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Cluster-robust inference: A guide to empirical practice
Small sample performance of indirect inference on DSGE models
Indirect inference and small sample bias - Some recent results
Sensitivity of sample for simulation-based reliability analysis methods
Large-sample inference on spatial dependence
Difference-in-differences inference with few treated clusters
Wild Bootstrap Randomization Inference for Few Treated Clusters
Finite sample inference for the maximum score estimand
Credible Granger-causality inference with modest sample lengths: A cross-sample validation approach
Randomization inference for difference-in-differences with few treated clusters
How cluster-robust inference is changing applied econometrics
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Cluster-robust inference: A guide to empirical practice
Small sample performance of indirect inference on DSGE models
Indirect inference and small sample bias - Some recent results