{"title":"非观测异质性参数的反加速和估计","authors":"Steven Stern, Yiyi Zhou","doi":"10.2139/ssrn.3297698","DOIUrl":null,"url":null,"abstract":"We show that, besides the well-known advantage of antithetic acceleratation reducing the variance of simulation error, it also reduces bias associated with estimating variance terms such as unobserved heterogeneity variance parameters. We provide proofs of the relevant asymptotics and empirical evidence from a sequence of Monte Carlo experiments.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"10 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Antithetic Acceleration and Estimation of Unobserved Heterogeneity Parameters\",\"authors\":\"Steven Stern, Yiyi Zhou\",\"doi\":\"10.2139/ssrn.3297698\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show that, besides the well-known advantage of antithetic acceleratation reducing the variance of simulation error, it also reduces bias associated with estimating variance terms such as unobserved heterogeneity variance parameters. We provide proofs of the relevant asymptotics and empirical evidence from a sequence of Monte Carlo experiments.\",\"PeriodicalId\":273058,\"journal\":{\"name\":\"ERN: Model Construction & Estimation (Topic)\",\"volume\":\"10 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Model Construction & Estimation (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3297698\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Model Construction & Estimation (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3297698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Antithetic Acceleration and Estimation of Unobserved Heterogeneity Parameters
We show that, besides the well-known advantage of antithetic acceleratation reducing the variance of simulation error, it also reduces bias associated with estimating variance terms such as unobserved heterogeneity variance parameters. We provide proofs of the relevant asymptotics and empirical evidence from a sequence of Monte Carlo experiments.