Semi-nonparametric competing risks analysis of recidivism
In this paper we specify a semi-nonparametric competing risks (SNP-CR) model of recidivism, for misdemeanors and felonies. The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi-nonpar...
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| Veröffentlicht in: | Journal of applied econometrics (Chichester, England) England), 2007-08, Vol.22 (5), p.971-993 |
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| container_end_page | 993 |
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| container_issue | 5 |
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| container_title | Journal of applied econometrics (Chichester, England) |
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| creator | Bierens, Herman J. Carvalho, Jose R. |
| description | In this paper we specify a semi-nonparametric competing risks (SNP-CR) model of recidivism, for misdemeanors and felonies. The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi-nonparametrically, using orthonormal Legendre polynomials on the unit interval, and integrated out to make the two durations dependent, conditional on the covariates. The SNP-CR model involved corresponds to a Logit model for felony arrest; hence the validity of the SNP-CR model can be tested by testing the validity of the implied Logit model. The latter will be done by using the integrated conditional moment (ICM) test. In the first instance we have estimated and tested two versions of the SNP-CR model, without and with fixed state effects. However, the ICM test rejects these models. Therefore, we have estimated and tested the model for each state separately. These state models are not rejected by the ICM test. Indeed, the estimation results vary substantially per state. |
| doi_str_mv | 10.1002/jae.960 |
| format | Article |
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The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi-nonparametrically, using orthonormal Legendre polynomials on the unit interval, and integrated out to make the two durations dependent, conditional on the covariates. The SNP-CR model involved corresponds to a Logit model for felony arrest; hence the validity of the SNP-CR model can be tested by testing the validity of the implied Logit model. The latter will be done by using the integrated conditional moment (ICM) test. In the first instance we have estimated and tested two versions of the SNP-CR model, without and with fixed state effects. However, the ICM test rejects these models. Therefore, we have estimated and tested the model for each state separately. These state models are not rejected by the ICM test. Indeed, the estimation results vary substantially per state.</description><identifier>ISSN: 0883-7252</identifier><identifier>EISSN: 1099-1255</identifier><identifier>DOI: 10.1002/jae.960</identifier><identifier>CODEN: JAECET</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Criminal arrests ; Criminology ; Datasets ; Econometric models ; Economic models ; Federal states ; Felony offenses ; Forecasts ; Misdemeanor offenses ; P values ; Parametric models ; Parole ; Polynomials ; Prisoners ; Recidivism ; Risk analysis ; Risk assessment ; Risk management ; Statistical models ; Studies ; U.S.A</subject><ispartof>Journal of applied econometrics (Chichester, England), 2007-08, Vol.22 (5), p.971-993</ispartof><rights>Copyright 2007 John Wiley & Sons, Ltd.</rights><rights>Copyright © 2007 John Wiley & Sons, Ltd.</rights><rights>Copyright Wiley Periodicals Inc. Aug 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5050-b7ff54bc7ddb488b04230eb9fc3952d6e7613265e976a593d57a33d48187663</citedby><cites>FETCH-LOGICAL-c5050-b7ff54bc7ddb488b04230eb9fc3952d6e7613265e976a593d57a33d48187663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/25146557$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/25146557$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,782,786,805,1419,27933,27934,45583,45584,58026,58259</link.rule.ids></links><search><creatorcontrib>Bierens, Herman J.</creatorcontrib><creatorcontrib>Carvalho, Jose R.</creatorcontrib><title>Semi-nonparametric competing risks analysis of recidivism</title><title>Journal of applied econometrics (Chichester, England)</title><addtitle>J. Appl. Econ</addtitle><description>In this paper we specify a semi-nonparametric competing risks (SNP-CR) model of recidivism, for misdemeanors and felonies. The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi-nonparametrically, using orthonormal Legendre polynomials on the unit interval, and integrated out to make the two durations dependent, conditional on the covariates. The SNP-CR model involved corresponds to a Logit model for felony arrest; hence the validity of the SNP-CR model can be tested by testing the validity of the implied Logit model. The latter will be done by using the integrated conditional moment (ICM) test. In the first instance we have estimated and tested two versions of the SNP-CR model, without and with fixed state effects. However, the ICM test rejects these models. Therefore, we have estimated and tested the model for each state separately. These state models are not rejected by the ICM test. Indeed, the estimation results vary substantially per state.</description><subject>Criminal arrests</subject><subject>Criminology</subject><subject>Datasets</subject><subject>Econometric models</subject><subject>Economic models</subject><subject>Federal states</subject><subject>Felony offenses</subject><subject>Forecasts</subject><subject>Misdemeanor offenses</subject><subject>P values</subject><subject>Parametric models</subject><subject>Parole</subject><subject>Polynomials</subject><subject>Prisoners</subject><subject>Recidivism</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Risk management</subject><subject>Statistical models</subject><subject>Studies</subject><subject>U.S.A</subject><issn>0883-7252</issn><issn>1099-1255</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNqF0MtKw0AUBuBBFKxVfAIhuNCFpM59MstSarwURSrobpgkE5k0lzqTqn17UyNdCOLqLM7Hfzg_AMcIjhCE-LLQZiQ53AEDBKUMEWZsFwxgFJFQYIb3wYH3BYSQQygGQM5NZcO6qZfa6cq0zqZB2lRL09r6NXDWL3yga12uvfVBkwfOpDaz79ZXh2Av16U3Rz9zCOZX06fJdTh7iG8m41mYMshgmIg8ZzRJRZYlNIoSSDGBJpF5SiTDGTeCI4I5M1JwzSTJmNCEZDRCkeCcDMFZn7p0zdvK-FZV1qemLHVtmpVXhHNJIyb-hUgKJqSQHTz9BYtm5boXvcLdTUYJ2qSd9yh1jffO5GrpbKXdWiGoNjWrrmbV1dzJi15-2NKs_2Lqdjzt9UmvC982bqsxQ5Sz7y_Cfm99az63e-0WigsimHq-j9WjFDG9E7F6IV9SkJSV</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Bierens, Herman J.</creator><creator>Carvalho, Jose R.</creator><general>John Wiley & Sons, Ltd</general><general>John Wiley & Sons</general><general>Wiley Periodicals Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>7U1</scope><scope>7U2</scope><scope>C1K</scope></search><sort><creationdate>200708</creationdate><title>Semi-nonparametric competing risks analysis of recidivism</title><author>Bierens, Herman J. ; Carvalho, Jose R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5050-b7ff54bc7ddb488b04230eb9fc3952d6e7613265e976a593d57a33d48187663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Criminal arrests</topic><topic>Criminology</topic><topic>Datasets</topic><topic>Econometric models</topic><topic>Economic models</topic><topic>Federal states</topic><topic>Felony offenses</topic><topic>Forecasts</topic><topic>Misdemeanor offenses</topic><topic>P values</topic><topic>Parametric models</topic><topic>Parole</topic><topic>Polynomials</topic><topic>Prisoners</topic><topic>Recidivism</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Risk management</topic><topic>Statistical models</topic><topic>Studies</topic><topic>U.S.A</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bierens, Herman J.</creatorcontrib><creatorcontrib>Carvalho, Jose R.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Journal of applied econometrics (Chichester, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bierens, Herman J.</au><au>Carvalho, Jose R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Semi-nonparametric competing risks analysis of recidivism</atitle><jtitle>Journal of applied econometrics (Chichester, England)</jtitle><addtitle>J. Appl. Econ</addtitle><date>2007-08</date><risdate>2007</risdate><volume>22</volume><issue>5</issue><spage>971</spage><epage>993</epage><pages>971-993</pages><issn>0883-7252</issn><eissn>1099-1255</eissn><coden>JAECET</coden><abstract>In this paper we specify a semi-nonparametric competing risks (SNP-CR) model of recidivism, for misdemeanors and felonies. The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi-nonparametrically, using orthonormal Legendre polynomials on the unit interval, and integrated out to make the two durations dependent, conditional on the covariates. The SNP-CR model involved corresponds to a Logit model for felony arrest; hence the validity of the SNP-CR model can be tested by testing the validity of the implied Logit model. The latter will be done by using the integrated conditional moment (ICM) test. In the first instance we have estimated and tested two versions of the SNP-CR model, without and with fixed state effects. However, the ICM test rejects these models. Therefore, we have estimated and tested the model for each state separately. These state models are not rejected by the ICM test. Indeed, the estimation results vary substantially per state.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/jae.960</doi><tpages>23</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Criminal arrests Criminology Datasets Econometric models Economic models Federal states Felony offenses Forecasts Misdemeanor offenses P values Parametric models Parole Polynomials Prisoners Recidivism Risk analysis Risk assessment Risk management Statistical models Studies U.S.A |
| title | Semi-nonparametric competing risks analysis of recidivism |
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