{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T19:59:02Z","timestamp":1791143942046,"version":"4.1.0"},"reference-count":27,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2009,2,1]],"date-time":"2009-02-01T00:00:00Z","timestamp":1233446400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2009,2]]},"abstract":"<jats:p>We propose generalized portmanteau-type test statistics in the frequency domain to test independence between two stationary time series. The test statistics are formed analogous to the one in the paper by Chen and Deo (2004,<jats:italic>Econometric Theory<\/jats:italic>20, 382\u2013416), who extended the applicability of the portmanteau goodness-of-fit test to the long memory case. Under the null hypothesis of independence, the asymptotic standard normal distributions of the proposed statistics are derived under fairly mild conditions. In particular, each time series is allowed to possess short memory, long memory, or antipersistence. A simulation study shows that the tests have reasonable size and power properties.<\/jats:p>","DOI":"10.1017\/s0266466608090063","type":"journal-article","created":{"date-parts":[[2009,1,9]],"date-time":"2009-01-09T14:13:57Z","timestamp":1231510437000},"page":"195-210","source":"Crossref","is-referenced-by-count":13,"title":["A GENERALIZED PORTMANTEAU TEST FOR INDEPENDENCE BETWEEN TWO STATIONARY TIME SERIES"],"prefix":"10.1017","volume":"25","author":[{"given":"Xiaofeng","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2009,2,1]]},"reference":[{"key":"S0266466608090063_ref25","unstructured":"Shao X . (2007) A Generalized Portmanteau Test for Independence between Two Stationary Time Series. 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