Arbeitspapier
A discrete model for bootstrap iteration
In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence converges to a random variable that follows the uniform U(0; 1) distribution, then the bootstrap is valid. The idea is studied by making the model under test discrete and finite, so that it is characterised by a finite three-dimensional array of probabilities. This device, when available, renders bootstrap iteration to any desired order feasible. It is used for studying a unit-root test for a process driven by a stationary MA(1) process, where it is known that the unit-root test, even when bootstrapped, becomes quite unreliable when the MA(1) parameter is in the vicinity of -1. Iteration of the bootstrap P value to convergence achieves reliable inference except for a parameter value very close to -1. The paper then endeavours to see these specific results in a wider context, and tries to cast new light on where bootstrap theory may be going.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP38/15
- Classification
-
Wirtschaft
Econometric and Statistical Methods and Methodology: General
Hypothesis Testing: General
Statistical Simulation Methods: General
- Subject
-
Bootstrap
bootstrap iteration
- Event
-
Geistige Schöpfung
- (who)
-
Davidson, Russell
- Event
-
Veröffentlichung
- (who)
-
Centre for Microdata Methods and Practice (cemmap)
- (where)
-
London
- (when)
-
2015
- DOI
-
doi:10.1920/wp.cem.2015.3815
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Davidson, Russell
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2015
Other Objects (12)
Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Bootstrap confidence sets under model misspecification
Linear time iteration
Wild bootstrap versus moment-oriented bootstrap
A bootstrap test for single index models
Bootstrap inference in single equation error correction models
Fast cluster bootstrap methods for linear regression models
Sieve bootstrap inference for time-varying coefficient models
Bootstrap methods for inference in the Parks model
Bootstrap approximations in a partially linear regression model
Bootstrap tests for overidentification in linear regression models
Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Bootstrap confidence sets under model misspecification
Linear time iteration
Wild bootstrap versus moment-oriented bootstrap
A bootstrap test for single index models
Bootstrap inference in single equation error correction models
Fast cluster bootstrap methods for linear regression models
Sieve bootstrap inference for time-varying coefficient models
Bootstrap methods for inference in the Parks model
Bootstrap approximations in a partially linear regression model
Bootstrap tests for overidentification in linear regression models
Nested Pseudo-likelihood Estimation and Bootstrap-based Inference for Structural Discrete Markov Decision Models
Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models
Bootstrap confidence sets under model misspecification
Linear time iteration
Wild bootstrap versus moment-oriented bootstrap
A bootstrap test for single index models
Bootstrap inference in single equation error correction models
Fast cluster bootstrap methods for linear regression models
Sieve bootstrap inference for time-varying coefficient models
Bootstrap methods for inference in the Parks model
Bootstrap approximations in a partially linear regression model