Sentences with phrase «residual autocorrelation»

Note that the purpose of the lag length is to eliminate all residual autocorrelation, so that the ADF tests can function properly.

All 741 regressions had a residual autocorrelation of about 1.0 x e ^ -15

Models were tested to ensure a lack of residual autocorrelation using the Ljung - Box portmanteau statistic [30], which indicated that all models discussed below had no residual structure (p ≥ 0.234).

Below I have linked to the images of the plots for anomaly trend, residual plots and residual autocorrelation (AR1) for the monthly, annual, January and June GISS NH temperature series for the time periods 1880 - 2007 and 1979 - 2007.

When I get the chance, I'll run this through Cochrane - Orcutt iteration to account for residual autocorrelation.

Not exact matches

There was some autocorrelation of the residuals, indicating that periods of under - and out - performance of equities over bonds tends to persist:

The estimator can be used to try to overcome autocorrelation and heteroscedasticity of the residuals, which can impact the standard errors and thus the calculated t - statistics and p - values.

In Exhibit 2, the sample autocorrelation function shows significant autocorrelation in the squared residual series, calculated by the square of daily total return of the S&P 500 subtracted by the long - term average daily return.

The autocorrelation structure might need a bit more work to accommodate long and short term serial correlation in residuals.

If the residual series generated by a model show no significant autocorrelation, as tested by Ljung - Box portmanteau statistics, then they are not distinguishable from white noise — a covariance stationary process.

From what I can tell, the statistical model used (sorry if I'm mistaken about this) doesn't allow for autocorrelation of residuals and simply treats El Nino and all other forms of internal variability as white noise.

I found this, http://www.xplore-stat.de/tutorials/xegbohtmlnode18.html, but it doesn't seem to match with Parker's «taking into account autocorrelation in the residuals».

Spatial autocorrelation (SAC) in the observed trend pattern is removed from the residuals by a well - specified explanatory model.

I then used the residuals and calculated the correlation, r, for AR1 autocorrelation and the goodness of fit of the residuals to a normal distribution.

By the way, there are striking differences in the appearance of the plots of the residuals of the regressions (not what you requested here) that make it rather easy to predict which will show better fits to a normal distribution and have less AR1 autocorrelation.

I have heard lately is a lot about the lack of temperature series fit to normal distributions and the presence of autocorrelation in the residuals of temperature anomalies and this post and some simple - minded analyses raise some questions in my mind.

We adjusted both the sample size and the degrees of freedom for indexing of the critical t - value according to the lag - 1 autocorrelation of the regression residuals.

I also recall that the RSS and UAH temperature annual temperature anomaly series residuals had little or no autocorrelation (AR1) in the time period 1979 - 2007.

That is what makes the regression residuals the important element in determining the autocorrelations and goodness of fit to normal distribution.

I calculated the Durbin Watson statistic (DW) for autocorrelation for the GISS time series 1979 - 2007 (using the residuals from the anomaly regression) for monthly data and determined a DW = 0.83 indicating a strong positive autocorrelation.

Looking at these results, that are admittedly anecdotal at this point, I see generally better fits to a normal distribution and lower autocorrelation (AR1) in the residuals as one goes from monthly to individual months to annual data series and as one goes to sub periods of a long term temperature anomaly series.

I have not done the monthly lag correlations for RSS, but when I did it for the GISS data 1979 - 2007, the DW statistic on the regression residuals showed a very significant positive autocorrelation.

Please note that when I subject an OLS regression of dT on ln CO2 for 1880 - 2008, and then perform Cochrane - Orcutt iteration on it to compensate for autocorrelation in the residuals, I still wind up with 60 % of variance accounted for when rho has dropped to an insignificant level.

Satisfaction of the assumption of a first - order Markov process was assessed by examination of the residuals of the lag - 1 regression, which were found to exhibit no further significant autocorrelation.

Uncertainty is estimated by the variance of the residuals about the fit, and accounts for serial correlation in the residuals as quantified by the lag - 1 autocorrelation.

But if you look at the residuals and test for the presence of autocorrelation you'll get very strong evidence that the error term is autocorrelated.

First Schmidt claims that M&M ought to have allowed for spatial autocorrelation, but appears to have confused autocorrelation in the dependent variable, which is not per se a problem, with autocorrelation in the residuals of the regression, which would be a problem if it existed in M&M.

McKitrick: Yes, but there is no spatial autocorrelation in hte model residuals, and that's the issue.