It was shown, in McIntyre and McKitrick (2005a, c),» and «This approach is similar to that of McIntyre and McKitrick (2005a, c) who use the full
empirical autocorrelation function to generate trend-less pseudo-proxies.»
The very inconsistency of the series within proxy networks such as Mann et al 2008 argues forcefully against the interpretation of high
empirical autocorrelation coefficients as being imported from a climate «signal», as opposed to being an inherent feature of the proxies themselves.
Not exact matches
But the theory makes other forecasts that are less consistent with
empirical evidence: that return distributions are Gaussian and broadly stationary; that there is no
autocorrelation spectrum and no cyclicality.
(Note that
empirical AR1 coefficients place less structure on the
autocorrelation than the hosking.sim simulations used with the NOAMER tree ring network in our 2005 simulations and simplify this aspect of this analysis.)
For such
empirical forecasts regionally depth - dependent damping rates \ (\ lambda (x, y, z) \) could be derived from the lag - 1
autocorrelation of long term observational records of water storage and soil moisture.