Joint and separate score tests for state dependence and unobserved heterogeneity
The paper compares separate, conditional, and joint score tests of duration dependence and unobserved heterogeneity when the null is the exponential model and the alternative is the heterogeneous Weibull model. The score tests based on the conditional score function include the Neyman C(α) test as a...
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Veröffentlicht in: | Journal of econometrics 1994, Vol.60 (1), p.273-291 |
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container_title | Journal of econometrics |
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creator | Jaggia, Sanjiv Trivedi, Pravin K. |
description | The paper compares separate, conditional, and joint score tests of duration dependence and unobserved heterogeneity when the null is the exponential model and the alternative is the heterogeneous Weibull model. The score tests based on the conditional score function include the Neyman
C(α) test as a special case. An examination of the non-null distribution of the joint test explains when all score tests have low power in the presence of multiple misspecifications. Monte Carlo experiments show that the conditional score tests are superior to the standard separate tests which confound unobserved heterogeneity and duration dependence. |
doi_str_mv | 10.1016/0304-4076(94)90047-7 |
format | Article |
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C(α) test as a special case. An examination of the non-null distribution of the joint test explains when all score tests have low power in the presence of multiple misspecifications. Monte Carlo experiments show that the conditional score tests are superior to the standard separate tests which confound unobserved heterogeneity and duration dependence.</description><subject>Applications</subject><subject>Biology, psychology, social sciences</subject><subject>C-alpha test</subject><subject>Comparative studies</subject><subject>Conditional score</subject><subject>Econometrics</subject><subject>Economic models</subject><subject>Economic theory</subject><subject>Exact sciences and technology</subject><subject>Insurance, economics, finance</subject><subject>Mathematics</subject><subject>Medical sciences</subject><subject>Monte Carlo simulation</subject><subject>Probability and statistics</subject><subject>Reliability, life testing, quality control</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>Test methods</subject><issn>0304-4076</issn><issn>1872-6895</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1994</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><sourceid>K30</sourceid><recordid>eNqFkUuLFDEUhQtxwHb0H7goVEQXNeZVeWwGZPA1NIwLXYdUcuNk6E7KJN3Q_97U9NALQQ3cJITvnhzu6boXGF1ghPl7RBEbGBL8rWLvFEJMDOJRt8JSkIFLNT7uVifkSfe0lDuE0MgkXXXfrlOItTfR9QVmk02FvtiUoa9Qaul9yn2py6uDGaKDaOGe3sU0Fch7cP0tVMjpJ0QI9fCsO_NmU-D5w3ne_fj08fvVl2F98_nr1Yf1YNvHdRDeME-Z9IIpoSYPjE7MMUxHORFnAREJaDScY0Kp5YhNyHmBxCidMoYQet69OerOOf3aNa96G4qFzcZESLuiqWSjasNp4Ms_wLu0y7F501hxPjZt8T8Ic0kWpVd_hYgSlHAmWKPYkbI5lZLB6zmHrckHjZFe4tJLFnrJQium7-PSi4P1sS23OdtTD7RlU0xbvdfUcNS2QyusWis1Yblq0va5FRFUE4X1bd02udcPXk2xZuOziTaUkyzDiHIsG3Z5xKBFtQ-QdbFhydiFDLZql8K_bf8GUDDAAQ</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>Jaggia, Sanjiv</creator><creator>Trivedi, Pravin K.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>North-Holland Pub. 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C(α) test as a special case. An examination of the non-null distribution of the joint test explains when all score tests have low power in the presence of multiple misspecifications. Monte Carlo experiments show that the conditional score tests are superior to the standard separate tests which confound unobserved heterogeneity and duration dependence.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/0304-4076(94)90047-7</doi><tpages>19</tpages></addata></record> |
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subjects | Applications Biology, psychology, social sciences C-alpha test Comparative studies Conditional score Econometrics Economic models Economic theory Exact sciences and technology Insurance, economics, finance Mathematics Medical sciences Monte Carlo simulation Probability and statistics Reliability, life testing, quality control Sciences and techniques of general use Statistics Test methods |
title | Joint and separate score tests for state dependence and unobserved heterogeneity |
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