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The new tests are asymptotically distribution-free, suitable when the conditioning set is infinite-dimensional, and consistent against a class of Pitman\u2019s local alternatives converging at the parametric rate<jats:italic>n<\/jats:italic><jats:sup>\u22121\/2<\/jats:sup>, with<jats:italic>n<\/jats:italic>the sample size. A Monte Carlo study shows that the simulated level of the proposed tests is close to the asymptotic level already for moderate sample sizes and that tests have a satisfactory power performance. Finally, we illustrate our methodology with an application to the well-known S&amp;P 500 daily stock index. The paper also contains an asymptotic uniform expansion for weighted residual empirical processes when initial conditions are considered, a result of independent interest.<\/jats:p>","DOI":"10.1017\/s0266466609990090","type":"journal-article","created":{"date-parts":[[2009,10,26]],"date-time":"2009-10-26T11:14:17Z","timestamp":1256555657000},"page":"744-773","source":"Crossref","is-referenced-by-count":18,"title":["ASYMPTOTIC DISTRIBUTION-FREE DIAGNOSTIC TESTS FOR HETEROSKEDASTIC TIME SERIES MODELS"],"prefix":"10.1017","volume":"26","author":[{"given":"J. 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