This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Sample Splitting and Threshold Estimation

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Bruce E. Hansen () (Boston College)

Additional information is available for the following registered author(s):

Abstract

Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuously-distributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for non-parametric function estimation. For example, the threshold autoregressive model (TAR) is popular in the non-linear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. The previous literature has demonstrated that threshold estimates are super-consistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either cross-section or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. It is found that the distribution of the threshold estimate is non- standard. Methods to construct asymptotic confidence intervals are introduced, using both a t-statistic and LR-statistic approach. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson (1995).

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://fmwww.bc.edu/EC-P/WP319.pdf
File Format: application/pdf
File Function: main text
Download Restriction: no

Publisher Info
Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 319..

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length: 38 pages
Date of creation: 01 Jan 1996
Date of revision: 12 May 1998
Handle: RePEc:boc:bocoec:319

Contact details of provider:
Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Phone: 617-552-3670
Fax: +1-617-552-2308
Email:
Web page: http://fmwww.bc.edu/EC/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F Baum).

Related research
Keywords:

Other versions of this item:

References listed on IDEAS
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.:
  1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November. [Downloadable!] (restricted)
  2. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March. [Downloadable!] (restricted)
    Other versions:
  3. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation, Yale University. [Downloadable!]
    Other versions:
  4. Hansen, Bruce E, 2002. "Tests for Parameter Instability in Regressions with I(1) Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 45-59, January.
    Other versions:
  5. repec:att:wimass:199419r is not listed on IDEAS
  6. repec:cup:etheor:v:7:y:1991:i:2:p:213-21 is not listed on IDEAS
  7. Bruce E. Hansen, 1998. "Testing for Structural Change in Conditional Models," Boston College Working Papers in Economics 310., Boston College Department of Economics. [Downloadable!]
    Other versions:
  8. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun. [Downloadable!] (restricted)
    Other versions:
  9. Chu, Chia-Shang James & White, Halbert, 1992. "A Direct Test for Changing Trend," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 289-99, July.
Full references

Cited by:
(explanations, 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.)
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.
Statistics
Access and download statistics

Did you know? The most prolific authors have over 400 items listed on IDEAS.

This page was last updated on 2008-7-24.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.