GOODNESS-OF-FIT TESTS FOR DOUBLY TRUNCATED DATA
GOODNESS-OF-FIT TESTS FOR DOUBLY TRUNCATED DATA

Summary/Abstract: Purpose – The paper regards the goodness-of-fit (GOF) tests for doubly truncatedcontinuous data with known truncation points. The first goal of the paper is to derive com-puting formulas of several test statistics for doubly truncated data, when the number oftruncated data is unknown. The second goal is to develop statistical inference procedurebased on the derived formulas, which includes information regarding the number oftruncated data, when it is available.Research method – The formulas and the inference procedure are developed with theuse of the methods proposed by Chernobai, Rachev and Fabozzi [2015], who alreadydeveloped GOF tests for the left truncated data, when the number of truncated data isunknown.Results – Several tests are developed in case of double truncation. Depending on thechosen truncation points, the tests for left, right or doubly truncated samples might beobtained. When no truncation occurs, the tests are reduced to the complete sample tests.The quality of the tests is assessed on the basis of the FTSE100 return distributions.Originality   / value   / implications   / recommendations – To the best knowledge of the author,computing formulas of the GOF test statistics for doubly truncated distributions withknown truncation points, when the number of truncated data is unknown, have not beenpresented in the literature yet.

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