Robustness of binary choice models to conditional heteroscedasticity

Tim Ginker, Offer Lieberman

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We show that when the true data generating process of a large class of binary choice models contains conditional heteroscedasticity, predictions based on the misspecified MLE in which conditional heteroscedasticity is ignored, are unaffected by the misspecification.

Original languageEnglish
Pages (from-to)130-134
Number of pages5
JournalEconomics Letters
Volume150
DOIs
StatePublished - 1 Jan 2017

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

Funding

Support from Israel Science Foundation Grant No. 1082-14 and from the Sapir Center in Tel Aviv University are gratefully acknowledged.

FundersFunder number
Sapir Center in Tel Aviv University
Israel Science Foundation1082-14

    Keywords

    • Conditional heteroscedasticity
    • Misspecified models
    • Probit

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