Least Absolute Deviation Estimation of a Shift
Jushan Bai ()
Econometric Theory, 1995, vol. 11, issue 03, pages 403-436
This paper develops the asymptotic theory for least absolute deviation estimation of a shift in linear regressions. Rates of convergence and asymptotic distributions for the estimated regression parameters and the estimated shift point are derived. The asymptotic theory is developed both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases. Asymptotic distributions are also obtained for trending regressors and for dependent disturbances. The analysis is carried out in the framework of partial structural change, allowing some parameters not to be influenced by the shift. Efficiency relative to least-squares estimation is also discussed. Monte Carlo analysis is performed to assess how informative the asymptotic distributions are.
References: Add references at CitEc
Citations View citations in EconPapers (26) Track citations by RSS feed
Downloads: (external link)
http://journals.cambridge.org/abstract_S026646660000935X link to article abstract page (text/html)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:cup:etheor:v:11:y:1995:i:03:p:403-436_00
Access Statistics for this article
More articles in Econometric Theory from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Series data maintained by Keith Waters ().