文章基本信息

标题：Bootstrapped Durbin– Watson Test of Autocorrelation for Small Samples
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作者：Jesmin Akter
期刊名称：ABC Journal of Advanced Research
印刷版ISSN：2304-2621
电子版ISSN：2312-203X
出版年度：2015
期号：2234
页码：68-72
出版社：Asian Business Consortium
摘要：The Durbin-Watson (DW) test is the most widely used test for autocorrelation of a first order in regression analysis. The critical value of DW test depends on X matrix. As a result, the DW test statistic falls sometime in the inconclusive region. For large sample, the DW test can be used for normal distribution. In this paper, we proposed a bootstrap critical value for small sample and compared the power properties with other procedures. Monte-Carlo study shows that the bootstrapped DW test performs better than the usual DW test with the help of power.