Tag Archives: Tamino

Response to Tamino’s ‘More Mathturbation’

I spent a fair amount of time responding to this fool’s diatribes regarding my post on the statistical significance of warming. I took offense at his title ‘More Mathturbation’, which I suspect that he found clever. He also tried to apply Dunning Kruger to myself. Bad move. Let’s see if he posts my response.

Update1: He has, although he doubled down on the gratuitous insults.

Update2: He is now refusing to approve my answers to the objections raised in the discussion. It’s pretty easy to win an argument when you don’t allow answers. LOL. He’s going to regret that one.

I also responded to AlexC over there and reproduced it here. Similar stupidity.

GreenHeretic’s Response to Tamino’s blog posting, ‘MORE MATHTURBATION’

Tamino: If you are going to critique someone else’s blog posting, especially with gratuitous insults, why isn’t it your practice to post something ‘over there’ to alert them? I don’t think much of your ethics.

Did you actually READ my post? Apparently not since you misrepresented why I rejected the Temp=f(CO2) relationship. True, I rejected the original model because of the strong autocorrelation of the errors. However, you are correct that such a deficiency can be ‘compensated’.

In the article I wrote, I rejected pursuing the question down that rabbit hole because CO2 explained no more than a simple time trend model. Real analysts with decades of modeling experience (like myself) understand the importance of that fact.

CO2 has no discernible incremental association with temperature beyond mere correlation over time. Nevertheless, I did waste considerable time exploring, but found nothing worth reporting. That led me to ask the question as to whether an actual trend existed. I am well aware of the dictum, ” It turns out that there isn’t, which is what the article demonstrated and concluded.

You claim that substituting an ARIMA(1,1,0) aka simple change model is not appropriate and say “I’ll bet he learned that in econometrics class.” I learned that in a graduate level advanced regression class in the late 1970s.

When I started analyzing weather in the energy sector in a professional capacity nearly twenty years ago, I validated the application of ARIMA methods for weather. My citation for the appropriateness of analyzing weather data using ARIMA is Daniel Wilks, ‘Statistical Methods in the Atmospheric Sciences’ published by Academic Press in 1995 (First Edition). It’s up to Third Edition today. You can find it easily enough on Amazon. Chapter 8 in my edition is entitled ‘Time Series’ should convince even you that my methodology is accepted by meteorology professionals.

I am not sure what your point was in your discussion of ‘unit root’. If you believe that my analysis has a problem with stationarity, then you should show it, with numbers. Hint: The problem when the dataset fails stationarity is that spurious regression relationships are reported, NOT when no regressions are reported. It’s clear that you have no idea what you are blathering about.

Your point regarding my lack of appropriateness tests for the ARIMA model is actually partially well taken. The ARIMA(1,1,0) shows an annoying negative residual autocorrelation at the fourth lag. A better fit model would have been to add a seasonal term. For other purposes, I would have done that. However, since it didn’t change the outcome (which was to check for statistical significance for the drift term in the ARIMA model), I didn’t include it.

As for your application of the so-called Dunning Kruger phenomenon, I suspect that you should really look in the mirror for the best example of that. You really haven’t a clue what you are talking about. Your multiple insults show a lack of maturity and lack of basic respect for those who disagree with you. Grow up.

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AlexC << However, he first differenced the data. Of course there is no significant trend leftover, because first differencing makes series stationary. This is really all that needs to be said >>

Question, AlexC: Did you PASS intermediate statistics? Had you been in the class that I TA’d, I would have failed you on that piece of your final exam. You don’t even understand the rudiments of an ARIMA model.

When you difference, you don’t eliminate the trend in any way. The trend ‘moves’ from a coefficient to become the constant, aka ‘drift’ in time series vernacular.

Update: AlexC has acknowledged my point on data differencing and trend analysis. Good for him. Unlike Tamino, he has integrity. Even if we disagree, I can respect him.