{"title":"家庭消费保险估算","authors":"A. Chatterjee, J. Morley, Aarti Singh","doi":"10.2139/ssrn.2933226","DOIUrl":null,"url":null,"abstract":"Blundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887-1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. Their estimate is imprecise and not robust to weighting scheme for GMM. We propose instead to use quasi maximum likelihood estimation (QMLE). It produces a more precise and significantly higher estimate of consumption insurance at 55%. For sub-groups by age and education, the differences between estimates are even more pronounced. Monte Carlo experiments with non-Normal shocks demonstrate that QMLE is more accurate than GMM.","PeriodicalId":23435,"journal":{"name":"UNSW Business School Research Paper Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Estimating Household Consumption Insurance\",\"authors\":\"A. Chatterjee, J. Morley, Aarti Singh\",\"doi\":\"10.2139/ssrn.2933226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Blundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887-1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. Their estimate is imprecise and not robust to weighting scheme for GMM. We propose instead to use quasi maximum likelihood estimation (QMLE). It produces a more precise and significantly higher estimate of consumption insurance at 55%. For sub-groups by age and education, the differences between estimates are even more pronounced. Monte Carlo experiments with non-Normal shocks demonstrate that QMLE is more accurate than GMM.\",\"PeriodicalId\":23435,\"journal\":{\"name\":\"UNSW Business School Research Paper Series\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"UNSW Business School Research Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2933226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"UNSW Business School Research Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2933226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887-1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. Their estimate is imprecise and not robust to weighting scheme for GMM. We propose instead to use quasi maximum likelihood estimation (QMLE). It produces a more precise and significantly higher estimate of consumption insurance at 55%. For sub-groups by age and education, the differences between estimates are even more pronounced. Monte Carlo experiments with non-Normal shocks demonstrate that QMLE is more accurate than GMM.
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