Simulation-based Tests that Can Use Any Number of Simulations
AbstractConventional procedures for Monte Carlo and bootstrap tests require that B, the number of simulations, satisfy a specific relationship with the level of the test. Otherwise, a test that would instead be exact will either overreject or underreject for finite B. We present expressions for the rejection frequencies associated with existing procedures and propose a new procedure that yields exact Monte Carlo tests for any positive value of B. This procedure, which can also be used for bootstrap tests, is likely to be most useful when simulation is expensive.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 1027.
Length: 12 pages
Date of creation: Oct 2004
Date of revision:
resampling; Monte Carlo test; bootstrap test; percentiles; simulation;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Russell Davidson & James MacKinnon, 2000.
"Bootstrap tests: how many bootstraps?,"
Taylor & Francis Journals, vol. 19(1), pages 55-68.
- James G. MacKinnon, 2006.
"Bootstrap Methods in Econometrics,"
1028, Queen's University, Department of Economics.
- Jeff Racine & James G. MacKinnon, 2006.
"Inference via kernel smoothing of bootstrap P values,"
1054, Queen's University, Department of Economics.
- Racine, Jeffrey S. & MacKinnon, James G., 2007. "Inference via kernel smoothing of bootstrap P values," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5949-5957, August.
- James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics.
- James G. MacKinnon, 2012. "Thirty Years of Heteroskedasticity-Robust Inference," Working Papers 1268, Queen's University, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Babcock).
If references are entirely missing, you can add them using this form.