{"62649":{"#nid":"62649","#data":{"type":"event","title":"T-statistic based correlation and heterogeneity robust inference, with applications to risk, inequality and concentration measurement","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E T-statistic based correlation and heterogeneity robust inference, with \napplications to risk, inequality and concentration measurement\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E Rustam Ibragimov\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMany risk, inequality, poverty and concentration measures are extremely \nsensitive to outliers, dependence, heterogeneity and heavy tails. In this \npaper we focus on robust measurement of risk, inequality, poverty and \nconcentration under heterogeneity, dependence and heavy-tailedness\nof largely unknown form using the recent results on t-statistic based \nheterogeneity and correlation robust inference in Ibragimov and Muller \n(2007). The robust large sample inference on risk, inequality, poverty and \nconcentration measures is conducted as follows: partition the observations \ninto q\u0026gt;=2 groups, calculate the empirical measures for each group and conduct \na standard test with the resulting q estimators of the population measures.\n\u003Cbr \/\u003E\n\u003Cbr \/\u003ENumerical results confirm the appealing\nproperties of tstatistic based robust inference method in this context, and \nits applicability to many widely used risk, inequality, poverty and \nconcentration measures, including Sharpe ratio; value at risk and expected \nshortfall; Gini coecient; Theil index, mean logarithmic deviation and \ngeneralized entropy measures; Atkinson measures; coecient of variation and \nHerfindahl-Hirschman index; head count, poverty gap and squared poverty gap \nindices and other Foster-Greer-Thorbecke measures of poverty, among others. \nThe results discussed in the paper further indicate a strong link\nbetween the tstatistic based robust inference methods and stochastic\nanalogues of the majorization conditions that are usually imposed on risk, \ninequality, poverty and concentration measures related to\n\u003Cbr \/\u003Eself-normalized sums or their transforms, as in the case of Sharpe ratio,\ncoefficient of variation and Herndahl-Hirschman index.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"T-statistic based correlation and heterogeneity robust inference, with applications to risk, inequality and concentration measurement"}],"uid":"27187","created_gmt":"2010-11-09 14:44:43","changed_gmt":"2016-10-08 01:53:24","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-11-11T10:00:00-05:00","event_time_end":"2010-11-11T11:00:00-05:00","event_time_end_last":"2010-11-11T11:00:00-05:00","gmt_time_start":"2010-11-11 15:00:00","gmt_time_end":"2010-11-11 16:00:00","gmt_time_end_last":"2010-11-11 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}