Risky Statistical Prediction Methods

A couple of days ago, Luke, a frequent commenter, sent in a number of links to a new Australian Government drought initiative. The Minister Tony Burke has appointed an Expert Panel to examine the social impacts of drought as part of its national review of exceptional circumstances (EC) funding, which argues for a major change, based on incentives rather than emergency aid. In a recent speech, Peter Kenny, chair of an expert panel looking at the social impact of drought said of the Drought Exceptional Circumstances Report (DECR):

The Bureau of Meteorology and the CSIRO predicted there was an increased risk of hotter and dryer seasons over the next 20 to 30 years, compared to the last hundred years, across many parts of Australia.

The same sort of restrained language is shown in the various news articles linked below. This is a far cry from the lurid claims of imminent drought apocalypse, encouraged by unvalidated (and completely inaccurate historically) climate model simulations produced by the DECR. After wringing the data out of them with the support of numerous blogs, I wrote a review showing that the frequency and severity of drought had actually declined over the whole of Australia, while the climate models show an increasing trend. This simple observation was not reported in the DECR. Contrary to the message in the report, the lead author later said in an interview that “a long term trend its not very clear in terms of exceptional low rainfall years.” CSIRO have been ‘statuesque’ in defense of their report. While the director promised a reply to my critique on Sept 16, I have yet to receive anything. In legal circles, no reply within 30 days can trigger a tacit admission that the accusations are true.

I have no issue at all with the way Peter Kenny seems to be reporting the CSIRO findings, and the latest report validates what I had always suspected about the political motivations for the dodgy statistical prediction methods in the DECR. In my post Scientists Biasing Research I proposed:

Could it be that climate scientists are biasing the detrimental effects of manmade global climate change to suit the review of EC funding by the Rudd government?

You have to wonder why people are listening to climate code red models when we know they are inherently flawed and useless at prediction. This speaks to the credibility of the media and the scientists involved. The skeptical bloggers seem to have it right and mainstream experts and the media have totally got this wrong.

The similarities with the sub-prime financial crisis are amazing. We don’t have to look far to find numerous financial bloggers warning about the dangers of unrestrained credit expansion while the mainstream economists and fund managers have been completely wrong-footed. They were probably using a lot of risky statistical prediction methods too. A recent paper, Forecasting the Depression: Harvard Versus Yale, tried a range of modern models at predicting the Great Depression, and finds that both the Harvard and Yale forecasters were systematically too optimistic. It seems that statistical models have not improved forecasting skill.

In the latest fiasco, The Federal Government initiated a bank deposit guarantee plan, and by Friday of last week 13 of the top 20 funds in Australia had frozen redemptions to investors to stop money flowing out of their businesses. I tend to agree a bit with the farewell letter of resignation from hedge fund manager Andrew Lahde, that some people are truly not worthy of the education they received.

My opinion based on many years in modeling, is that the mainstream scientists exaggerate the predicted global warming effects on purpose. They have vested interests, as bad news sells science. Governments get to blame their present land management mistakes on something in the future outside their control. This is why validation is so important and statistical prediction methods need to be way down the feeding chain on sources of evidence.

Who does this remind you of?

Below are a set of links to the recent policy initiative on drought management.

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0 thoughts on “Risky Statistical Prediction Methods

  1. David,

    Missing from the commentary about economic modelluing prediction is the Austrian School’s assertion that it is intrinsically impossible. I won’t go into the reasoning behind this except to point any one who doubts this to read Mises argument on this.

    What economists have done is to misapply the methods used in the physical sciences, which work, to the social sciences, in which the methods cannot.

  2. David,

    Missing from the commentary about economic modelluing prediction is the Austrian School’s assertion that it is intrinsically impossible. I won’t go into the reasoning behind this except to point any one who doubts this to read Mises argument on this.

    What economists have done is to misapply the methods used in the physical sciences, which work, to the social sciences, in which the methods cannot.

  3. I haven’t heard it said that the Austrian school finds economics unpredictable. I thought the credit expansion, malinvestment, crash was very predictable.

    I am skeptical of assertions that things aren’t predictable. The economics paper I mention on the Great Depression argues that because none of the models predict it, then it is inherently unpredictable (so the experts at Yale and Harvard can’t be blamed). I find the same vain logic in the climate modellers that justify the high variation, and large bounds for estimates of CO2 sensitivity with ‘intrinsic system variability’, chaos etc. It much more likely that the constraints on the system are poorly understood, and when added, as Miskolczi has done, yield much more precise estimates.

    Never attribute to the real system something that can be attributed to human ignorance.

  4. I haven’t heard it said that the Austrian school finds economics unpredictable. I thought the credit expansion, malinvestment, crash was very predictable.

    I am skeptical of assertions that things aren’t predictable. The economics paper I mention on the Great Depression argues that because none of the models predict it, then it is inherently unpredictable (so the experts at Yale and Harvard can’t be blamed). I find the same vain logic in the climate modellers that justify the high variation, and large bounds for estimates of CO2 sensitivity with ‘intrinsic system variability’, chaos etc. It much more likely that the constraints on the system are poorly understood, and when added, as Miskolczi has done, yield much more precise estimates.

    Never attribute to the real system something that can be attributed to human ignorance.

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