@article{ref1,
title="Working with misspecified regression models",
journal="Journal of quantitative criminology",
year="2018",
author="Berk, Richard and Brown, Lawrence and Buja, Andreas and George, Edward and Zhao, Linda",
volume="34",
number="3",
pages="633-655",
abstract="OBJECTIVES Conventional statistical modeling in criminology assumes proper model specification. Very strong and unrebutted criticisms have existed for decades. Some respond that although the criticisms are correct, there is for observational data no alternative. In this paper, we provide an alternative.<br><br>METHODS We draw on work in econometrics and statistics from several decades ago, updated with the most recent thinking to provide a way to properly work with misspecified models.<br><br>RESULTS We show how asymptotically, unbiased regression estimates can be obtained along with valid standard errors. Conventional statistical inference can follow.<br><br>CONCLUSIONS If one is prepared to work with explicit approximations of a &quot;true&quot; model, defensible analyses can be obtained. The alternative is working with models about which all of the usual criticisms hold.<p /> <p>Language: en</p>",
language="en",
issn="0748-4518",
doi="10.1007/s10940-017-9348-7",
url="http://dx.doi.org/10.1007/s10940-017-9348-7"
}