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Very understandable as the climate change deniers have no model of their own and they probably wouldn’t even know where to start.

“Climate change deniers” do not want a model, they just want facts. One would be the MEASUREMENT of the temperature increase or decrease caused by greenhouse gases.
Contributors here are not “deniers” but “questioners” and researchers who seek answers. There are many knowledgeable contributors to this weblog, likely a higher number than most other blogs.
Would you say that the statistical connection between CO2 and temperature is “inane”?

The greater part of the commentary at this site pertains to proxy reconstructions, rather than climate models. Proxy reconstructions of the Mannian type or the Steig climate reconstruction rely entirely on statistical correlation.

If you choose to regard Steig et al 2009, Mann et al 2008 and similar literature as built on “inane statistical correlations”, then you are obviously free to do so and you will find that few readers here will disagree with you.

I’m not sure what point you believe that Hendry was “missing”. Hendry’s article was written in the early 1980s, long before IPCC, and addressed the problem of spurious correlation and the difficulty that it presented for standard statistical tests. Hendry is a distinguished econometrician and your name-calling here shows more about you than about him.

BTW, UK rainfall as a predictor of GDP is not a model so much as a correlation. Models usually come form first principles and unless you have something that follows a reasonable and logical causality, I wouldn’t count it as a model. Yet, similar inane statistical correlations is what the ClimateAudit seem to treat as their bread and butter, and it doesn’t surprise me that Hendry misses the point entirely.

I would suggest that models used for hurricane risk and hurricane event forecasting have an additional inherent risk factor that officially and/or unofficially considers the failure of over and under predicting these events and consequences. Given the initial choice of a model that might under predict or over predict, the over prediction, I judge, has the least adverse consequences if incorrect in the opposite direction.

The confidences and narrow uncertainties that climate scientists claim for their climate models forecasts, and for that matter, the climate scientist consensus on AGW are predicated as much, in my view, on what they see as the consequences of being wrong as on any hard analysis on a scientific level. I would suggest that most of these climate scientists see little adverse unintended consequences of AGW mitigation (unlike myself) and many of them would judge the mitigation actions worthy even without the issue of AGW.

Keynes may have been in tune with mass psychology in some areas, but that would not necessarily extent to his ability in being rather blind (I hope) to the reaction of politicians to what his economic model called for in good economic times when politicians were expected to balance their budgets. Keynes also may have seen the adverse effects of others models, but not his own. That is perhaps human nature. In my view stagflation broke the Keynesian model in the US in the 1970s for all to see, but there are no doubts that that model is being applied aggressively in current time.

…a correlation so imposing that is puzzling that it was neglected by Steig et al 2009 and Mann et al 2008.

Good grief! Don’t give them any more ideas.

Why complain? The problem is explicitly stated and it has not been fixed. Would you continue to drive your car without further comment when you knew it had a flat?

I placed it online when we discussed (here) Hendry’s Econometrics – Alchemy or Science, in which Hendry also discussed using cumulative UK rainfall as a predictor (“proxy”) for the Consumer Price Index, a correlation so imposing that is puzzling that it was neglected by Steig et al 2009 and Mann et al 2008.

LOL. You just cannot resist the rib, eh?