Filipski, Mateusz

AgEcon Search - Research in Agricultural & Applied Economics

Give to AgEcon SearchThe World’s Largest Open Access Agricultural & Applied Economics Digital LibraryThis document is discoverable and free to researchers across the globe due to the work of AgEcon Search.Help ensure our sustainability.AgEcon Searchhttp://ageconsearch.umn.eduaesearch@umn.eduPapers downloaded from AgEcon Search may be used for non-commercial purposes and personal study only. No other use, including posting to another Internet site, is permitted without permission from the copyright owner (not AgEcon Search), or as allowed under the provisions of Fair Use, U.S. Copyright Act, Title 17 U.S.C.No endorsement of AgEcon Search or its fundraising activities by the author(s) of the following work or their employer(s) is intended or implied.      Discussion of Session Titled, “Machine Learning in Agricultural Trade and Policy Settings”    Mateusz Filipski       Selected Paper prepared for presentation at the International Agricultural Trade Research Consortium’s (IATRC’s) 2019 Annual Meeting: Recent Advances in Applied General Equilibrium Modeling: Relevance and Application to Agricultural Trade Analysis, December 8-10, 2019, Washington, DC.       Copyright 2019 by Mateusz Filipski. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.  Machine Learning in Agricultural Trade andPolicy Settings:Discussion on 3 presented papersbyMateusz FilipskiUGA, IFPRIIATRC, Washington DC, Dec 9, 2019Outline2/16Overview of the sessionA couple of slides on each paperTying it all togetherThree papers3/16Predicting trade flows with Decision Trees / Neural Networks (Gopi)Predicting famines with Decision Trees (Kathy)Predicting Yields with (Semiparametric) Neural Networks (Joe)... Notice a pattern?Three papers3/16Predicting trade flows with Decision Trees / Neural Networks (Gopi)Predicting famines with Decision Trees (Kathy)Predicting Yields with (Semiparametric) Neural Networks (Joe)... Notice a pattern?The world of ML4/16The world of ML5/16Navigating the world of ML6/16Trade flows paper: What I love about it7/16Completely new look at a (very) old questionLeverages the volumes of data we haveUses known theory (gravity model) but frees us from structure.Trade flows paper: What I’m missing8/16Plot the data and draw a treeAre you really using the right model as comparison? (the PPML)You can defy gravity!Trade flows paper: What I’m missing8/16Plot the data and draw a treeAre you really using the right model as comparison? (the PPML)You can defy gravity!Trade flows paper: Let’s Defy Gravity!9/16Still a bilateral framework i/jMaybe country k matters?Eg: USA ⇒ Brazil/China tradeFamines paper: What I love about it10/16Major issue, huge potential.Can address the “Where” question with high precision (+ who?)Leverages existing HH data + satellite data ⇒ cheap.Famines paper: What I’m missing11/16Are you really using the right decision tool for comparison? (a logit)LSMS as ground truth is small data (esp. at cluster-level)HH variables limit your ability to expand (ex: flooring material?)Very few high-frequency variables matter in the resultsFamine paper: High-frequency variables12/16Most important “high-frequencyvariable” is sixth.Should show how results vary overtime (say, 5 maps at different times,with different clusters identified asinsecure)Yield paper: What I love about it13/16(focusing just on the ML part of the paper)Pioneers the upcoming changes: ML for inputs to chained models (here:crop + cost models).All input data is remote-sensed: easily scalable and re-usableFairly mature methodologyYield paper: What I’m missing14/16ML not always superior to simple OLS.Superiority of ML is very regional. This suggests there is a major missingvariable.What difference does the ML really make in terms of the final projections?It’s the end of the world as we know it...15/16With ML, Prediction becoming central: β ⇒ ŷIncremental changes: better inputs for existing modelsSeismic changes: completely new ways of dealing with data / modelsExciting time to think about new ways of doing things... And I feel fine! It’s the end of the world as we know it...15/16With ML, Prediction becoming central: β ⇒ ŷIncremental changes: better inputs for existing modelsSeismic changes: completely new ways of dealing with data / modelsExciting time to think about new ways of doing things... And I feel fine! ML is not without pitfalls16/16Source: https://xkcd.com/1838/

Machine Learning in Agricultural Trade and Policy Settings: Discussion on 3 presented papers

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