{"id":8984,"date":"2025-12-15T13:09:50","date_gmt":"2025-12-15T16:09:50","guid":{"rendered":"https:\/\/aaep.org.ar\/?p=8984"},"modified":"2025-12-15T13:09:51","modified_gmt":"2025-12-15T16:09:51","slug":"ask-think-predict-llm-based-nowcasting-of-argentinas-gdp","status":"publish","type":"post","link":"https:\/\/aaep.org.ar\/?p=8984","title":{"rendered":"Ask, Think, Predict: LLM-Based Nowcasting of Argentina\u2019s GDP"},"content":{"rendered":"<iframe loading=\"lazy\" class=\"wonderplugin-pdf-iframe\" src=\"https:\/\/aaep.org.ar\/wp-content\/plugins\/wonderplugin-pdf-embed\/pdfjslight\/web\/viewer.html?v=2&file=https:\/\/aaep.org.ar\/works\/works2025\/4806.pdf\" width=\"100%\" height=\"600px\" style=\"border:0;\"><\/iframe>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We assess the forecasting performance of Gemini 2.0 Flash Thinking Experimental with Apps for Argentina\u2019s seasonally adjusted quarter-over-quarter real GDP growth over 2021\u20132024. Using a transparent prompt-engineering protocol, we elicit point forecasts at three horizons and evaluate them in real time against the Central Bank\u2019s Relevamiento de Expectativas de Mercado (REM). Across data vintages, Gemini delivers accuracy comparable to expert consensus\u2014especially at nowcast and one-quarter-ahead horizons\u2014while operating at effectively zero marginal cost. We also document where performance deteriorates (regime shifts and data revisions) and show that simple prompt safeguards improve stability. Overall, general-purpose LLMs can complement conventional workflows by providing competitive short-horizon forecasts with minimal implementation overhead.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[29],"tags":[33],"class_list":["post-8984","post","type-post","status-publish","format-standard","hentry","category-anales","tag-aaep-anales-2025"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ask, Think, Predict: LLM-Based Nowcasting of Argentina\u2019s GDP - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ask, Think, Predict: LLM-Based Nowcasting of Argentina\u2019s GDP - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"og:description\" content=\"We assess the forecasting performance of Gemini 2.0 Flash Thinking Experimental with Apps for Argentina\u2019s seasonally adjusted quarter-over-quarter real GDP growth over 2021\u20132024. Using a transparent prompt-engineering protocol, we elicit point forecasts at three horizons and evaluate them in real time against the Central Bank\u2019s Relevamiento de Expectativas de Mercado (REM). Across data vintages, Gemini delivers accuracy comparable to expert consensus\u2014especially at nowcast and one-quarter-ahead horizons\u2014while operating at effectively zero marginal cost. We also document where performance deteriorates (regime shifts and data revisions) and show that simple prompt safeguards improve stability. 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