Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/55171 
Year of Publication: 
2009
Series/Report no.: 
Working Paper No. 645
Publisher: 
Queen Mary University of London, Department of Economics, London
Abstract: 
A new test is proposed for the null of absence of serial correlation. The test uses a data-driven smoothing parameter. The resulting test statistic has a standard limit distribution under the null. The smoothing parameter is calibrated to achieve rate-optimality against several classes of alternatives. The test can detect alternatives with many small correlation coefficients that can go to zero with an optimal adaptive rate which is faster than the parametric rate. The adaptive rate-optimality against smooth alternatives of the new test is established as well. The test can also detect ARMA and local Pitman alternatives converging to the null with a rate close or equal to the parametric one. A simulation experiment and an application to monthly financial square returns illustrate the usefulness of the proposed approach.
Subjects: 
absence of serial correlation
data-driven nonparametric tests
adaptive rate-optimality
small alternatives
time series
JEL: 
C12
C32
Document Type: 
Working Paper

Files in This Item:
File
Size
604.24 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.