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Long version: His statistical methods are perfectly standard for panel data analysis. The specification search is kind of sloppy, but his robustness checks seem thorough and come out fine, so I don't think that's a problem. Overall, I like his statistical methods and would probably use similar analysis myself.
The problems start to show up in the interpretation. The correct interpretation of his data is that:
- Jake Friends come and go. Enemies accumulate.
The normalised load factor for UK onshore wind farms declines from a peak of about 24% at age 1 to 15% at age 10 and 11% at age 15.
If he is saying that, on average a park that starts at 24% will decline after 10 years to 15%, and after 15 years to 11% (which is what his rhetoric would lead us to believe), then that is clearly false.
If, on the other hand, he is saying that the average load factor for all farms at 1 year is 24%, and that the average for farms of 10 and 15 years are 15 and 11%, then that is something entirely different, which I don't believe is supported by the data either (having already caught him out on Danish offshore).
There is so much month to month variability in the onshore data that you would have to do statistics, what me not know how. But just squinting at the data is enough for me. He's a liar, and if you can explain how he comes up with this particular lie, I would be very interested. It is rightly acknowledged that people of faith have no monopoly of virtue - Queen Elizabeth II
I'm not totally clear in how that would give him the sort of results he's getting, but models with autocorrelated residuals take great delight in producing interesting and innovative forms of gibberish.
Did the author provide any justification for using Statistics to Model and analyze dynamic, non-linear, phenomena subject to sensitivity to initial conditions (aka "wind")? She believed in nothing; only her skepticism kept her from being an atheist. -- Jean-Paul Sartre
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