Type: Seminar

Modified: Mon 22 Sep 2008 1505; Tue 23 Sep 2008 0959

Distribution: World

Expiry: 26 Sep 2008

Auth: johnr(.ststaff;3005.3001)@p8224.pc.maths.usyd.edu.au

********************************************** * * * UNIVERSITY OF SYDNEY * * * * SCHOOL OF MATHEMATICS & STATISTICS * * * * STATISTICS SEMINAR SERIES - 2008 * * * ********************************************** **************************** * SEMINAR NOTICE * **************************** -------------------------------------------------------------------------- Structural Nonparametric Cointegrating Regression Dr Qiying Wang Friday 26 September 2pm Carslaw 173 Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near-integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics useable in practical work. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near-integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required. Please visit: http://www.maths.usyd.edu.au/u/StatSeminar/ for more information about past and coming seminars. Enquiries about the Statistics Seminar: John Robinson: johnr@maths.usyd.edu.au