Testing for the mixture hypothesis of conditional geometric and exponential distributions

Jin Seo Cho; Jin Seok Park; Sang Woo Park

Testing for the mixture hypothesis of conditional geometric and exponential distributions

Jin Seo Cho, Jin Seok Park, Sang Woo Park

Department of Economics

Research output: Contribution to journal › Article › peer-review

Abstract

This study examines the mixture hypothesis of conditional geometric distributions using a likelihood ratio (LR) test statistic based on that used for unconditional geometric distributions. As such, we derive the null limit distribution of the LR test statistic and examine its power performance. In addition, we examine the interrelationship between the LR test statistics used to test the geometric and exponential mixture hypotheses. We also examine the performance of the LR test statistics under various conditions and confirm the main claims of the study using Monte Carlo simulations. ecosystems.

Original language	English
Pages (from-to)	1-27
Number of pages	27
Journal	Journal of Economic Theory and Econometrics
Volume	29
Issue number	2
Publication status	Published - 2018 Jun

Bibliographical note

Funding Information:
The author is most grateful to the editor, Jin Lee. Cho acknowledges the support of the Yonsei University Future-leading Research Initiative of 2017 (2017-22-0090).

Publisher Copyright:
© 2018, Korean Econometric Society. All rights reserved.

All Science Journal Classification (ASJC) codes

Economics and Econometrics

Cite this

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title = "Testing for the mixture hypothesis of conditional geometric and exponential distributions",

abstract = "This study examines the mixture hypothesis of conditional geometric distributions using a likelihood ratio (LR) test statistic based on that used for unconditional geometric distributions. As such, we derive the null limit distribution of the LR test statistic and examine its power performance. In addition, we examine the interrelationship between the LR test statistics used to test the geometric and exponential mixture hypotheses. We also examine the performance of the LR test statistics under various conditions and confirm the main claims of the study using Monte Carlo simulations. ecosystems.",

author = "Cho, {Jin Seo} and Park, {Jin Seok} and Park, {Sang Woo}",

note = "Funding Information: The author is most grateful to the editor, Jin Lee. Cho acknowledges the support of the Yonsei University Future-leading Research Initiative of 2017 (2017-22-0090). Publisher Copyright: {\textcopyright} 2018, Korean Econometric Society. All rights reserved.",

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PY - 2018/6

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N2 - This study examines the mixture hypothesis of conditional geometric distributions using a likelihood ratio (LR) test statistic based on that used for unconditional geometric distributions. As such, we derive the null limit distribution of the LR test statistic and examine its power performance. In addition, we examine the interrelationship between the LR test statistics used to test the geometric and exponential mixture hypotheses. We also examine the performance of the LR test statistics under various conditions and confirm the main claims of the study using Monte Carlo simulations. ecosystems.

AB - This study examines the mixture hypothesis of conditional geometric distributions using a likelihood ratio (LR) test statistic based on that used for unconditional geometric distributions. As such, we derive the null limit distribution of the LR test statistic and examine its power performance. In addition, we examine the interrelationship between the LR test statistics used to test the geometric and exponential mixture hypotheses. We also examine the performance of the LR test statistics under various conditions and confirm the main claims of the study using Monte Carlo simulations. ecosystems.

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Testing for the mixture hypothesis of conditional geometric and exponential distributions

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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