Tuesday , 24 May 2022

Economic Forecasters Are Consistently Wrong – Here’s Proof & Why It Is Important to Know

New research shows that forecasters tend to underestimate the expected outcome offorecasting crystal ball anticipated economic data for several months in a row, and then overestimate it for several months in a row thereafter. Why does that matter? Read on.

So writes Lydia DePillis (washingtonpost.com/blogs/wonkblog/) in edited excerpts from her original article* titled Forecasters are bad at forecasting, study finds.

[The following article is presented by  Lorimer Wilson, editor of www.FinancialArticleSummariesToday.com and www.munKNEE.com and the FREE Market Intelligence Report newsletter (sample here – register here) and may have been edited ([ ]), abridged (…) and/or reformatted (some sub-titles and bold/italics emphases) for the sake of clarity and brevity to ensure a fast and easy read. This paragraph must be included in any article re-posting to avoid copyright infringement.]

DePillis goes on to say in further edited (and in some instances paraphrased) excerpts:

A few days before important economic data comes out — housing starts, unemployment, durable goods orders, etc. — analysts guess where they’re going to end up. Usually, they’re not right…and…[according to a]…research from a team at Goldman Sachs…forecasts tend to underestimate the outcome for several months in a row, and then overestimate it for several months in a row [as is illustrated in the chart below].

Screen Shot 2013-08-08 at 11.18.42 AM

It’s surprising that if the forecasts were overly optimistic in one month that they’re more likely to be overly optimistic the next month as well because, while you would expect economists to adjust their forecasts based on being wrong the last time, they seem to think they were still close to right [and continue doing so] until they finally change their minds and overcompensate in the other direction and guess what? The phenomenon seems to have gotten more pronounced in recent years.
[Below is a chart showing] the measure of the “autocorrelation,” or the degree to which one consensus forecast will match up with the previous one:

Screen Shot 2013-08-08 at 1.24.13 PM

That probably happened because the economic shocks of the last four years have just been so much greater than those of the relatively smooth years before them, and analysts have a harder time adjusting to structural changes.

Why does…[the above] matter? In part, because:

  1. markets react to the difference between whether an indicator exceeds or disappoints analysts’ expectations so if you know that the forecast is more likely to be wrong in the same way that it was last month, you can make bets on the market reaction that are more likely to be right.
  2. its also an interesting window into people who are supposed to be able to tell where the economy is going have their own blind spots. It will be fascinating to note, after realizing that bias, whether they adjust to get rid of it.
[Editor’s Note: The author’s views and conclusions in the above article are unaltered and no personal comments have been included to maintain the integrity of the original post. Furthermore, the views, conclusions and any recommendations offered in this article are not to be construed as an endorsement of such by the editor.]

*http://www.washingtonpost.com/blogs/wonkblog/wp/2013/08/08/forecasters-are-bad-at-forecasting-study-finds/ (© 1996-2013 The Washington Post)

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