{"id":591756,"date":"2019-12-16T05:08:08","date_gmt":"2019-12-16T05:08:08","guid":{"rendered":"https:\/\/enterpriseam.com\/egypt\/2019\/12\/16\/overcoming-selection-bias-with-econometrics\/"},"modified":"2019-12-16T05:08:08","modified_gmt":"2019-12-16T05:08:08","slug":"overcoming-selection-bias-with-econometrics","status":"publish","type":"post","link":"https:\/\/enterpriseam.com\/egypt\/2019\/12\/16\/overcoming-selection-bias-with-econometrics\/","title":{"rendered":"Overcoming selection bias with econometrics"},"content":{"rendered":"

Here\u2019s something we haven\u2019t explored much on Enterprise: Econometrics<\/strong>, or the use of statistics to make sense of economic data, has an uncanny power to separate fact from fiction. The use of regression models is often able to overcome the notorious \u201cselection bias,\u201d which makes the \u201csearch for causal knowledge\u201d all the more complicated. In this episode of YouTube series \"Mastering Econometrics\" (watch, runtime: 9:32<\/a>), MIT professor Josh Angrist breaks down how the discipline can overcome selection bias. Most notably, regression allows econometricians to look at the effect of one variable on another while isolating all others, creating a situation known as ceteris paribus, which is Latin for \"other things equal.\u201d Angrist illustrates this using the example of wage differences between graduates of state-owned universities and those who studied at private universities.<\/p>\n","protected":false},"excerpt":{"rendered":"

Here\u2019s something we haven\u2019t explored much on Enterprise: Econometrics, or the use of statistics to make sense of economic data, has an uncanny power to separate fact from fiction. The use of regression models is often able to overcome the notorious \u201cselection bias,\u201d which makes the \u201csearch for causal knowledge\u201d all the more complicated. In […]<\/p>\n","protected":false},"author":1,"featured_media":591757,"comment_status":"closed","ping_status":"open","sticky":false,"template":"single-feature.php","format":"standard","meta":{"_acf_changed":false,"newspack_ads_suppress_ads":false,"newspack_popups_has_disabled_popups":false,"newspack_sponsor_sponsorship_scope":"","newspack_sponsor_native_byline_display":"inherit","newspack_sponsor_native_category_display":"inherit","newspack_sponsor_underwriter_style":"inherit","newspack_sponsor_underwriter_placement":"inherit","ep_exclude_from_search":false,"_primary_brand":0,"newspack_featured_image_position":"","newspack_post_subtitle":"","newspack_article_summary_title":"Overview:","newspack_article_summary":"","newspack_hide_updated_date":false,"newspack_show_updated_date":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[9004],"tags":[72,2388],"brand":[],"newspack_spnsrs_tax":[],"class_list":["post-591756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-worth-watching","tag-enterpriseam","tag-worth-watching","wpautop","entry"],"acf":{"mongo_id":"49dc9c8d-9139-4f44-8a12-3e6d0e98df6a","is_powered_by":true,"story_type":"1","photo_url":"https:\/\/ent.news\/2019\/12\/2816.jpg","photo_position":"above","related_issue":[591734],"teaser":"","voice_url":""},"yoast_head":"\nOvercoming selection bias with econometrics - EnterpriseAM Egypt<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/enterpriseam.com\/egypt\/2019\/12\/16\/overcoming-selection-bias-with-econometrics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Overcoming selection bias with econometrics\" \/>\n<meta property=\"og:description\" content=\"Here\u2019s something we haven\u2019t explored much on Enterprise: Econometrics, or the use of statistics to make sense of economic data, has an uncanny power to separate fact from fiction. The use of regression models is often able to overcome the notorious \u201cselection bias,\u201d which makes the \u201csearch for causal knowledge\u201d all the more complicated. 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