ADAPTIVE SEMIPARAMETRIC ESTIMATION IN THE PRESENCE OF AUTOCORRELATION OF UNKNOWN FORM

F. Javier Hidalgo

Journal of Time Series Analysis, 1992, vol. 13, issue 1, 47-78

Abstract: Abstract. In a time series regression model the residual autoregression function is an unknown, possibly non‐linear, function. It is estimated by non‐parametric kernel regression. The resulting least‐squares estimate of the regression function is shown to be adapative, in the sense of having the same asymptotic distribution, to first order, as estimates based on knowledge of the autoregression function. Also, a Monte Carlo experiment about the behaviour of the estimator is described.

Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9892.1992.tb00094.x

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:bla:jtsera:v:13:y:1992:i:1:p:47-78

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().