Bootstrapping a time series model
Abstract
The bootstrap is a methodology for estimating standard errors. The idea is to use a Monte Carlo simulation experiment based on a nonparametric estimate of the error distribution. The main objective of this dissertation was to demonstrate the use of the bootstrap to attach standard errors to coefficient estimates and multi-period forecasts in a second-order autoregressive model fitted by least squares and maximum likelihood estimation. A secondary objective of this article was to present the bootstrap in the context of two econometric equations describing the unemployment rate and individual income tax in the state of Oklahoma. As it turns out, the conventional asymptotic formulae (both the least squares and maximum likelihood estimates) for estimating standard errors appear to overestimate the true standard errors. But there are two problems: 1) the first two observations y/sub 1/ and y/sub 2/ have been fixed, and 2) the residuals have not been inflated. After these two factors are considered in the trial and bootstrap experiment, both the conventional maximum likelihood and bootstrap estimates of the standard errors appear to be performing quite well. At present, there does not seem to be a good rule of thumb for deciding when the conventional asymptotic formulae willmore »
- Authors:
- Publication Date:
- Research Org.:
- Oklahoma State Univ., Stillwater (USA)
- OSTI Identifier:
- 5596975
- Resource Type:
- Thesis/Dissertation
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; BOOTSTRAP MODEL; TIME-SERIES ANALYSIS; ERRORS; LEAST SQUARE FIT; MONTE CARLO METHOD; COMPOSITE MODELS; MATHEMATICAL MODELS; MATHEMATICS; MAXIMUM-LIKELIHOOD FIT; NUMERICAL SOLUTION; PARTICLE MODELS; 658000* - Mathematical Physics- (-1987)
Citation Formats
Son, M S. Bootstrapping a time series model. United States: N. p., 1984.
Web.
Son, M S. Bootstrapping a time series model. United States.
Son, M S. 1984.
"Bootstrapping a time series model". United States.
@article{osti_5596975,
title = {Bootstrapping a time series model},
author = {Son, M S},
abstractNote = {The bootstrap is a methodology for estimating standard errors. The idea is to use a Monte Carlo simulation experiment based on a nonparametric estimate of the error distribution. The main objective of this dissertation was to demonstrate the use of the bootstrap to attach standard errors to coefficient estimates and multi-period forecasts in a second-order autoregressive model fitted by least squares and maximum likelihood estimation. A secondary objective of this article was to present the bootstrap in the context of two econometric equations describing the unemployment rate and individual income tax in the state of Oklahoma. As it turns out, the conventional asymptotic formulae (both the least squares and maximum likelihood estimates) for estimating standard errors appear to overestimate the true standard errors. But there are two problems: 1) the first two observations y/sub 1/ and y/sub 2/ have been fixed, and 2) the residuals have not been inflated. After these two factors are considered in the trial and bootstrap experiment, both the conventional maximum likelihood and bootstrap estimates of the standard errors appear to be performing quite well. At present, there does not seem to be a good rule of thumb for deciding when the conventional asymptotic formulae will give acceptable results.},
doi = {},
url = {https://www.osti.gov/biblio/5596975},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 1984},
month = {Sun Jan 01 00:00:00 EST 1984}
}