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ScottIrwinUI's profile
Scott Irwin
Scott Irwin
Scott Irwin
@ScottIrwinUI

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Scott Irwin

@ScottIrwinUI

Agricultural Economist at the University of Illinois; Lifelong fascination with commodity markets; Iowa farmboy

Champaign, Illinois
farmdoc.illinois.edu/irwin/
Joined September 2012

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    Scott Irwin‏ @ScottIrwinUI Nov 9

    1. Gather round the campfire kids. Today we are going to talk about smoothing in USDA crop yield estimates. This is a staple of the chatter after every USDA crop report but the way the stats are reported and discussed is often wrong.

    7:26 AM - 9 Nov 2018
    • 6 Retweets
    • 27 Likes
    • Mark Alan Jirik Nicole Kyle Zarate Ellyn Ferguson P.E.Marchand 🌽 Tim Gueldener 🌱 Tom Evans Arthur Liming James Plummer
    3 replies 6 retweets 27 likes
      1. New conversation
      2. Scott Irwin‏ @ScottIrwinUI Nov 9

        2. We start with what is known as a 2x2 contingency table in stats. Here is the table for Nov-Oct change in USDA corn yield estimates vs. Jan-Nov change in USDA estimates over 1988-2017.pic.twitter.com/K6oAcGyrao

        1 reply 0 retweets 4 likes
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      3. Scott Irwin‏ @ScottIrwinUI Nov 9

        3. 29 USDA crop reports 1988-2017 (missed Oct 2013). If distribution of changes was random, the numbers in each of the 2x2 cells would be 7 or 8 (no decimals as these are counts). Hopefully, it is obvious that the distribution of counts is not random.pic.twitter.com/5M6zmGKQKx

        1 reply 0 retweets 3 likes
        Show this thread
      4. Scott Irwin‏ @ScottIrwinUI Nov 9

        4. What you usually see in the trade is adding up the top left and bottom right cells (18) and dividing by the total count (29) to get a probability. This is 62.1% for corn. Nothing technically wrong with this but it is not really what we want to know.pic.twitter.com/NpM6sR2N6J

        1 reply 0 retweets 5 likes
        Show this thread
      5. Scott Irwin‏ @ScottIrwinUI Nov 9

        5. What we really want to know is a conditional probability. If Nov-Oct change is negative what is the probability that Jan-Nov will be negative? Table below computes exactly these conditional probabilities.pic.twitter.com/GG38cbCPHN

        1 reply 0 retweets 4 likes
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      6. Scott Irwin‏ @ScottIrwinUI Nov 9

        6. Now we are getting somewhere. Since 1988, given that Nov-Oct change in USDA corn yield estimate was negative there is a 70% chance that the Jan-Nov yield change is negative. So, the odds definitely favor the USDA dropping the US corn yield again in Jan.pic.twitter.com/nspvJjN418

        1 reply 1 retweet 4 likes
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      7. Scott Irwin‏ @ScottIrwinUI Nov 9

        7. Here is the same 2x2 contingency table counts for USDA soybean yield estimatespic.twitter.com/qbXZ68iAJb

        1 reply 0 retweets 4 likes
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      8. Scott Irwin‏ @ScottIrwinUI Nov 9

        8. And here is the conditional probability table for USDA soybean yield estimates. Since 1988, given that Nov-Oct change in USDA soy yield estimate was negative there is a 64% chance that the Jan-Nov yield change is negative. Odds favor USDA dropping the US soy yield in Jan.pic.twitter.com/bQrANZMuxR

        2 replies 0 retweets 3 likes
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      9. Scott Irwin‏ @ScottIrwinUI Nov 9

        9. Now, the really big question is why is there such a large degree of smoothing in changes to USDA corn and soybean yield estimates through time? Statistical forecasting theory says the changes from month-to-month should be unpredictable, or random.

        1 reply 0 retweets 3 likes
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      10. Scott Irwin‏ @ScottIrwinUI Nov 9

        10. I have been thinking about this for a long, long time. Here is the abstract to an AJAE article on the topic back in 2006. https://academic.oup.com/ajae/article/88/4/1091/78348 …pic.twitter.com/zWIoMRkJOv

        1 reply 0 retweets 3 likes
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      11. Scott Irwin‏ @ScottIrwinUI Nov 9

        11. Here is the abstract of my most recent paper (h/t on both papers to my amazing co-authors) from 2013 in the JAAE. http://ageconsearch.umn.edu/record/143639/files/jaae486.pdf …pic.twitter.com/9a7IkTyYhs

        2 replies 1 retweet 3 likes
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      12. Scott Irwin‏ @ScottIrwinUI Nov 9

        12. In short, I can't find any evidence that big crops get bigger and small crops get smaller in terms of USDA corn and soybean yield estimates. But, the USDA does tend to smooth yield estimates from month-to-month regardless of crop size. And it has been going on for decades.

        1 reply 2 retweets 6 likes
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      13. Scott Irwin‏ @ScottIrwinUI Nov 9

        13. Is the conservatism in changes in USDA corn and soybean yield estimates due to USDA processes or in the survey data they receive? I think it is some of both. Farmers probably slowly change yield estimates and USDA does not want to jerk the market around.

        2 replies 0 retweets 6 likes
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      14. Scott Irwin‏ @ScottIrwinUI Nov 9

        14. That is my two cents (well maybe ten bucks) on the topic of changes in USDA yield estimates. For some reason, just had to get that off my chest this morning.

        2 replies 0 retweets 5 likes
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      15. Scott Irwin‏ @ScottIrwinUI Nov 9

        15. I am going to add a definition of forecast smoothing courtesy of Peoples Grain @ad8871: "smoothing," the USDA spreads its forecast adjustments across multiple reporting periods instead of reporting the entire adjustment immediately

        1 reply 0 retweets 2 likes
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      16. Scott Irwin‏ @ScottIrwinUI Nov 9

        16. Also, Peoples Grain @ad8871 makes another point. When conditional probability is 70% you have a 30% change of being wrong. Always good to be reminded of that. Thanks!

        1 reply 0 retweets 4 likes
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      17. End of conversation
      1. Nick Starkey‏ @nstark07 Nov 9
        Replying to @ScottIrwinUI

        As usual, great stuff in this thread from @ScottIrwinUI

        0 replies 0 retweets 2 likes
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      1. The Dipstick Guy  🔧‏ @AllanDikeman Nov 9
        Replying to @ScottIrwinUI

        Great info! You would think with all the data we have floating around in the cloud, the forecast would be more accurate. It would be nice to anonymously yield to a data base.

        0 replies 0 retweets 1 like
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