Hermite regression analysis of multi-modal count data
We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case...
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| Veröffentlicht in: | Economics bulletin 2010, Vol.30 (4), p.2936-2945 |
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| container_title | Economics bulletin |
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| creator | Giles, David E. A |
| description | We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models. |
| format | Article |
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A</creator><creatorcontrib>Giles, David E. A</creatorcontrib><description>We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models.</description><identifier>ISSN: 1545-2921</identifier><identifier>EISSN: 1545-2921</identifier><language>eng</language><publisher>AccessEcon</publisher><subject>Count data ; financial crises ; multi-modal data ; over-dispersion</subject><ispartof>Economics bulletin, 2010, Vol.30 (4), p.2936-2945</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3994,4010</link.rule.ids><backlink>$$Uhttp://econpapers.repec.org/article/eblecbull/eb-10-00512.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Giles, David E. A</creatorcontrib><title>Hermite regression analysis of multi-modal count data</title><title>Economics bulletin</title><description>We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models.</description><subject>Count data</subject><subject>financial crises</subject><subject>multi-modal data</subject><subject>over-dispersion</subject><issn>1545-2921</issn><issn>1545-2921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNpNjM1KAzEYAIMoWKvvkBeI5LfZPUpRKxR60XPIzxeNJLslyR769i7owdMMc5grtGFKKsJHzq7_-S26a-2bUi44YxukDlBL6oArfFZoLc0TtpPNl5YaniMuS-6JlDnYjP28TB0H2-09uok2N3j44xZ9vDy_7w_keHp92z8dSWVcdqKFEnYIXArhpIo0auXjjlHmnXZrcYpxzm0YRx-VG6XcuTAE0INXWnIIYosef78VzuDNuaZi68WAy-DdkvNqhlFD6ToyX72IH3f_RvY</recordid><startdate>2010</startdate><enddate>2010</enddate><creator>Giles, David E. A</creator><general>AccessEcon</general><scope>DKI</scope><scope>X2L</scope></search><sort><creationdate>2010</creationdate><title>Hermite regression analysis of multi-modal count data</title><author>Giles, David E. A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-r124t-7353a8d2433b45f0f75cf6101cb7bb45b51222ad99cf5b9446bd8de78c5742ed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Count data</topic><topic>financial crises</topic><topic>multi-modal data</topic><topic>over-dispersion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Giles, David E. A</creatorcontrib><collection>RePEc IDEAS</collection><collection>RePEc</collection><jtitle>Economics bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Giles, David E. A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hermite regression analysis of multi-modal count data</atitle><jtitle>Economics bulletin</jtitle><date>2010</date><risdate>2010</risdate><volume>30</volume><issue>4</issue><spage>2936</spage><epage>2945</epage><pages>2936-2945</pages><issn>1545-2921</issn><eissn>1545-2921</eissn><abstract>We discuss the modeling of count data whose empirical distribution is both multi-modal and over-dispersed, and propose the Hermite distribution with covariates introduced through the conditional mean. The model is readily estimated by maximum likelihood, and nests the Poisson model as a special case. The Hermite regression model is applied to data for the number of banking and currency crises in IMF-member countries, and is found to out-perform the Poisson and negative binomial models.</abstract><pub>AccessEcon</pub><tpages>10</tpages></addata></record> |
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| identifier | ISSN: 1545-2921 |
| ispartof | Economics bulletin, 2010, Vol.30 (4), p.2936-2945 |
| issn | 1545-2921 1545-2921 |
| language | eng |
| recordid | cdi_repec_primary_eblecbull_eb_10_00512_htm |
| source | RePEc; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Count data financial crises multi-modal data over-dispersion |
| title | Hermite regression analysis of multi-modal count data |
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