{"content":[{"text":"COMPARATIVE STUDY OF MORTALITY FORECASTING \n MODELS","bbox":{"left":0.193109,"top":0.148754,"width":0.6142320000000001,"height":0.041911000000000004}},{"text":"Steven Haberman & Arthur Renshaw","bbox":{"left":0.306655,"top":0.218374,"width":0.38716400000000006,"height":0.022813}},{"text":"Cass Business School \n City University \n 106 Bunhill Row \n London EC1Y 8TZ","bbox":{"left":0.661072,"top":0.25291,"width":0.1840830000000001,"height":0.06302399999999997}},{"text":"S.Haberman@city.ac.uk","bbox":{"left":0.649718,"top":0.328088,"width":0.199596,"height":0.017917000000000016}},{"text":"Abstract","bbox":{"left":0.453782,"top":0.391819,"width":0.08638000000000001,"height":0.022813000000000028}},{"text":"A number of mortality forecasting models have been proposed in \n the literature in recent years. We consider the age-period model \n proposed by lee and Carter (1992) and the age-period-cohort \n extension proposed by Renshaw and Haberman (2006) as well as \n the range of age-period-cohort models put forward by Cairns et al \n (2007, 2008). We investigate the properties of 6 models from \n these families and then, using a dataset relating to the mortality \n experience of England and Wales (1961-2007), undertake a \n comparative investigation of the goodness of fit of the 6 models, \n the resulting predictions and measures of uncertainty. The results \n will be of interest to insurance companies and others with \n significant annuity and defined benefit pension liabilities.","bbox":{"left":0.151261,"top":0.443766,"width":0.688102,"height":0.23309400000000002}},{"text":"Keywords: binomial response model; mortality forecasting; age- \n period-cohort effects; back-fitting; model comparisons.","bbox":{"left":0.151261,"top":0.692318,"width":0.6618390000000001,"height":0.04190700000000003}},{"text":"Theme 1: biometric risks and their securitisation.","bbox":{"left":0.151261,"top":0.749683,"width":0.502934,"height":0.022812999999999972}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}