Efficient Semiparametric Prediction Intervals

Bryan W. Brown
Additional contact information
Bryan W. Brown: Rice University

No 1633, Econometric Society World Congress 2000 Contributed Papers from Econometric Society

Abstract: The construction of prediction intervals and regions and their probability content for nonlinear systems with nonparametric disturbances is considered. The semiparametric efficiency bound for estimating the probability content of a known interval (region) and estimators that attain the bound are developed. Semiparametric efficient estimation of optimal prediction intervals (regions) which either (i) maximize probability content given interval length (region area) or (ii) maximize interval length (region area) given probability content is studied. The estimated probability content of (i) is found to have the same limiting behavior as if the interval (region) were known with certainty and hence attains the semiparametric efficiency bound. Further, the estimated probability of the estimated interval (region) approximates the true coverage probability to order root-n for (i) but order smaller than root-n for (ii). A Monte Carlo experiment is conducted to compare the new predictors to competitors.

Date: 2000-08-01
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://fmwww.bc.edu/RePEc/es2000/1633.pdf main text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecm:wc2000:1633

Access Statistics for this paper

More papers in Econometric Society World Congress 2000 Contributed Papers from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().