Bootstrap Hypothesis Testing
By James G. MacKinnon. 2007
262 citations recorded by the source corpus.
This paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point.
Subfields: Economics and Econometrics, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/68909009ecafb1147e766a1b3db58e0733864613
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Morten Ørregaard Nielsen
- James G. MacKinnon
- Matthew D. Webb
- Martin Huber
- Giovanni Mellace
- D. Roodman
- David Ruppert
- Elizabeth Tipton
In Finance
- Morten Ørregaard Nielsen
- James G. MacKinnon
- Matthew D. Webb
- D. Roodman
- Martin Huber
- Giovanni Mellace
- David Ruppert
- Katsumi Shimotsu
In Statistics and Probability
- Morten Ørregaard Nielsen
- James G. MacKinnon
- Matthew D. Webb
- D. Roodman
- Martin Huber
- Giovanni Mellace
- David Ruppert
- Ping Li
Citations in the corpus
- RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections (2014, background): “The reasons for this preference are well-known (see MacKinnon (2009), and references therein).”
- Fast and wild: Bootstrap inference in Stata using boottest (2018, methodology): “Simulation results in MacKinnon, Nielsen, and Webb (2017) favor clustering in the dimension with the fewest clusters, which, in this case, is the G = 49 countries.”
- Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints (2015, background): “As for instance discussed in MacKinnon (2007), the Bonferroni inequality implies that the p-value for joint hypotheses can be computed by multiplying the minimum p-value by the number of constraints, in our case four.”
- Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications (2015, unknown): “…random samples of the modified data are drawn, the test statistic of interested computed on each of them, and the resulting distribution is the estimated distribution of the test-statistic under the null (Bickel & Ren, 2001; Boos, 2003; MacKinnon, 2009; Paparoditis & Politis, 2005; Romano, 1989).”
- Hypothesis Testing in Econometrics (2010, unknown): “See Manski (2003) for numerous examples.”