## Monday, September 16, 2013

### ## Adjusting temperature series stats for autocorrelation.

I've been writing a lot lately on autocorrelation for temperature time series. This is basically a methods post.

I've often referred to a post by Hu McCulloch, in which he reproves a scientist for quoting just a OLS regression for a monthly series with OLS CI's, and shows how the CI's are much larger if the residuals are assumed to be not white noise but AR(1) correlated. He gives a useful set of formulae, and describes in particular the Quenouille AR(1) correction to the trend uncertainty. This seems to be the current standard, and it is what I use in my trend viewer.

Stats program R has an arima(xreg=) function which will give trends with error estimate for general ARIMA models. It solves a slightly different optimisation for regression, with a different trend for each ARIMA type. But for AR(1), the results are similar to the Quenouille approach.

So why bother with approximations? I'm interested for two reasons, in connection with the trend viewer. In my weekly updates, I do about 5 million regressions. That takes a bit over an hour, but only because I avoid the individual regression summations, instead using differences of cumulative sums calculated for each dataset. You can't to that in a package. arima() would take many hours.

The other reason is that to provide interactive output, I have to be able to repeat the calculation in Javascript. Time isn't an issue there, but programming simplicity is.

In this post, I'll describe some extensions which allow Quenouille style corrections to more complex ARMA() models. I also give corrections to the OLS trend. The trend corrections are O(1/N) (N=#data) and I believe the error is then O(1/N^2), and for the CI's, O(1/N). Those errors are for the approximation to the stochastic model - whether it is the right model is a greater uncertainty.

With these formulae the complete procedure is
1. Do a normal OLS regression.
2. Calculate the residuals and estimate the first few autocorrelations (one lag for A(1)m 2 for AR(2) and ARMA(1,1) etc)
3. Use the Yule-Walker equations to estimate the ARMA parameters. That just solves a few linear equations in the AR() parameters.
4. Correct the trend and uncertainty using thge formulae given here.

#### Fitting stochastic models

I'm talking about fitting to regression residuals, so it is simpler to have a regression already done, although I described a method for simultaneously fitting regression and residual model. An ARMA model for regression residuals can be written: A(L)d=M(L)ε Here A is a polynomial in the lag operator L, A=1-A1*L-A2*L^2... applied to the time series d (so L d_i = d_{i-1}) M(L) is a similar polynomial in L: M(L)=1+M1*L+... ε is a vector of iid random variables The series d is taken to be the residuals of a regression on data y: d=y-a-b*x. The notation ARMA(j,k) means that A is of order j and M of order k. The effect of applying the polynomial A to d can also be represented by the lower triangular matrix (AR(2) example) A=
 ...0 -A2 -A1 1 0 0... ...0 0 -A2 -A1 1 0... ...0 0 0 -A2 -A1 1...
This is of course a fragment - if d has length n then A is (n-j) x n and is Toeplitz. M also corresponds to such a matrix. The regression equation can be written M-1L d = ε Comprehensive programs like the R arima() will minimise d* K* K d where K = M-1L over coefficients a and b, and return the appropriate uncertainties. The alternative described above is to minimise d* d, and then adjust the coefficients and their uncertainty.

The regression of the ARMA model gives trend b = y* KK x/ x* KK x, (KK = K* K) assuming K x has zero mean. But KK is symmetric, and also near Toeplitz, deviating near the top left and bottom right corners. The i'th row of KK (far from corners) is symmetric about the i'th value (with unsymmetric truncation). But the arithmetic progression x can be split into a constant xi and a part x-xi that is antisymmetric about i. The latter yields a zero sum with that row of KK, so the result is R*x_i, where R is the (constant near centre) rowsum of KK. So KK * x ≈ R * x This argument gives b = y* KK x/ x* KK x ≈ y* x/ x* x which is the OLS value. That's why only a small adjustment is needed - it is due to end effects. In the case of AR(n) models, the deviation of KK from Toeplitz only affects the top n and bottom n rows, so it is a fairly simple matter to add up the missing terms. The corrections to the OLS trend for the various models are, for time (=x) series y of length N:
 OLS or AR(0) b AR(1) b+(3*b+6*(y_N*A1+y_1)/(1-A1)/(x_N-x_1))/N AR(2) b+6*(b+(y_1*(1-A1)+y_2+A2*y_(N-1)+y_N*(A1+A2))/(1-A1-A2)/(x_N-x_1))/N
And here are the results comparing these approximations with values computed with the R arima() program, setting xreg and using method="CSS".The corrected values are much closer than the OLS trend. Trends are from 1980 to July 2013.

 Data Hadcrut4 GISSLO NOAA UAH MSU.RSS OLS 1.5643 1.5626 1.4878 1.4101 1.2979 AR(1) 1.5743 1.5661 1.4936 1.4257 1.307 AR(1) approx 1.5742 1.5662 1.4936 1.4257 1.3073 AR(2) 1.5946 1.5909 1.5072 1.4705 1.3392 AR(2) approx 1.5959 1.5907 1.5079 1.4696 1.3389

#### Trend uncertainties.

The original adjustment for AR(1) autocorrelated regression residuals was given by Quenouille (1952), and has been widely used. In an earlier post, I gave the corresponding approximations for ARMA(1,1) and AR(2). I'll exrend to AR(3) and give the derivations here. Note that whereas for the trend we were looking for an O(1/N) adjustment, here it is O(1) - ie much bigger.

So it is sufficient to find an uncertainty adjustment for either the OLS trend or the true AR() trend. OLS is simpler.

Our ARMA model is
A(L)d=M(L)ε ε is iid
or, reverting to matrices
d=A-1M ε
The variance of the OLS trend b is then estinated by
var(b)= x*C x/x* x
where x is time (mean removed) and C is the estimated covariance of d. In the model, C=M*A*-1A-1M.
Now C is symmetric, and apart from end effects, Toeplitz, reflecting stationarity. That means that each row is symmetric about the diagonal. So when the i'th row of C multiplies x, the effect of the part that is odd about xi makes no contribution. But the even part is just the constant . So the effect of applying C to x is just to multiply by the constant rowsum of C (apart from O(1/N) end effects). The ignoring of end effects here requires that there is only moderate autocorrelation, so C is diagonally dominant.

Each row of C is just the diagonal value (zero-lag variance of d) multiplied by the acf. That diagonal value divided by x*x. So the correction required to the variance of b is just (with our 1/N caveats) the sum of the autocovariance function.

Now it is good to go back to polynomials. The (Toeplitz) matrix product M*M is equivalent to the polynomial M(L-1)*M(L). And the acf is almost just the set of coefficients of the Laurent series got by expanding M(L-1)*M(L)/(A(L-1)*A(L)) in powers of L. A Laurent series is just a power series with both positive and negative powers.

I say almost, because the zero'th order coefficient of that series is not 1. So we have to find it and divide by it. This could be done with messy series expansion calcs, but it is neater with complex analysis, replacing L by z.

The Laurent series may converge in an annulus in the complex plane, and here it converges on the unit circle. To get the central coeficient, divide by z and integrate on that contour. It's a rational function, so that just means summing the residues inside the unit circle.

How to do that? For the previous post I did the algebra by hand, but for AR(3) I resorted to Mathematica, and I'll describe that process. I write A(z)=(1-az)(1-bz)(1-cz), so
1/A(1/z)/A(z)/z=z2/((z-a)(z-b)(z-c)(1-az)(1-bz)(1-cz))
I don't need to ever find the roots a,b,c, and they don't have to be real. To get the residue at a, I define f(a,b,c)=a2/((a-b)(a-c)(1-a^2)(1-ba)(1-ca))
Then I ask Mathematica to Simpliify[f[a,b,c]+f[b,c,a]+f[c,a,b]]
It comes back with a polynomial in the numerator and factors in the denominator, symmetric of course in a,b,c. The factors in the denominator I can reassemble as values of A(z) at specific points. The numerator I can recognise symmetrics like ab+bc+ca, and re-express these as coefficients of A.
Let A0 be the result - the zero'th order coefficient of 1/A(1/z)/A(z). Then the numbers in the acf are just the Laurent coefficients of 1/A(1/z)/A(z)/A0, and the sum - the Quenouille correction - is just 1/A(1)/A(1)/A0.

So here are some results. First the formulae for the Quenouille factors for adjustment of trend uncertainty:

 AR(1) (1+A1)/(1-A1)=A(-1)/A(1) ARMA(1,1) A(-1)/A(1)*(1+M1)2/(1+M12+2*M1*A1)) AR(2) A(-1)/A(1)*(1+A2)/(1-A2))) AR(3) A(-1)/A(1)*(1+A2+(A1-A3)*A32)/((1-A2-(A1+A3)*A32))

And here are the numerical results for the monthly data, period 1980-July 2013. First the OLS trend and standard error; then the various models, with in each case the R arima() trend and se, and then the se as calculated by the above Quenouille-style approx.

 Result HADCRUT4 GISS NOAA UAH MSU-RSS OLS trend β 1.7020 1.7092 1.7500 1.4086 1.5676 OLS s.e. β 0.0463 0.0651 0.0514 0.0665 0.0641 AR(1) trend β 1.6984 1.7084 1.7449 1.4161 1.5678 AR(1) s.e. β 0.0785 0.0936 0.0765 0.1101 0.0978 AR(1) Quen s.e. 0.0781 0.0933 0.0762 0.1096 0.0974 ARMA(1,1) trend β 1.6953 1.7025 1.7423 1.4188 1.5687 ARMA(1,1) s.e. β 0.0946 0.1132 0.0936 0.1189 0.1027 ARMA(1,1) Quen s.e. 0.0940 0.1123 0.0930 0.1182 0.1022 AR(2) trend β 1.6854 1.6831 1.7363 1.4221 1.5756 AR(2) s.e. β 0.0929 0.1103 0.0899 0.1228 0.1045 AR(2) Quen s.e. 0.0925 0.1106 0.0894 0.1217 0.1037 AR(3) trend β 1.6893 1.6853 1.7395 1.4098 1.5673 AR(3) s.e. β 0.0908 0.1060 0.0901 0.1073 0.0971 AR(3) Quen s.e. 0.0900 0.1059 0.0893 0.1060 0.0961

#### Conclusion

For autocorrelated data, the ordinary OLS trend analysis will get the trend near right with a O(1/N) error, which can be calculated. But the standard error will be substantially underestimated. There are at least three options
• Use a package like arima() in R, which probably uses a general optimisation algorithm
• Use the Newton-Raphson-style algorithm that I described here (I'm planning an update with more systematic code). This is fast and does everything at once. It is easy to code in R, but not so much in, say, Javascript.
• Calculate and correct the OLS trends, using Yule-Walker equations to estimate model coefficients.
The approximations will deteriorate with vary high autocorrelation. Then the best remedy is to instead use, say, annual averages. Little information will be lost, and the autocorrelation will reduce.

1. A good and interesting post. Have you consider publishing this? Or maybe discuss it with some stats guys, e.g. on stats.stackexchange.com?

A small typo:
I think you mean R's arime() function rather than amira.

best regards
SRJ

1. Thanks, SRJ. Yes, results are accumulating, and I should try to publish them. I have some a statistical colleague who I talked to about it, but I have the impression that interest in statistical significance of such trends is probably greatest in the climate area at the moment. But perhaps Eugene Seneta could help me with it.

Thanks for noting the typo - I used to work with a min ing organisation called AMIRA, and it's a mistake I often make.

2. Oops, I made another typ, R's arima() function is what I mean

SRJ

3. Nick, with your skills you should give the Southern Oscillation Index (SOI) a go in terms of correcting the temperature series.

The issue is that the particular autocorrelation profile stems from various stochastic properties all mixed together. The better objective may be to try to deconvolve the overall behavior into individual potentially separable behaviors. What will be left may be uncorrelated white noise.

First pull out the SOI from the trend, then pull out the volcanic disturbances, then the TSI variations, etc.

That is what I tried to do in this experiment:
http://imageshack.us/a/img69/9159/hpi.gif

IMO, the connection between the SOI and global average temperature is a gold mine waiting to be analyzed in detail. But beware that the fake skeptics don't like anyone looking into this as it removes the pause and it quantifies the natural variability.

1. WHT,
I did that in combination here. I didn't show SOI separately. My fits don't seem quite as good as yours.

2. Nick, Don't give up on the SOI. I used the data set from
http://www.cgd.ucar.edu/cas/catalog/climind/soi.html

I was intrigued after I saw others producing their own model fits in the comments at
http://www.skepticalscience.com/pacific-ocean-global-warming-puzzle-Kosaka-Xie.html

4. Hello,

Nick, do you have tested and what do you think about : "Third order autoregressive
integrated model with drift (ARIMA(3,1,0) " ? ( See this paper : http://www.metoffice.gov.uk/media/pdf/2/3/Statistical_Models_Climate_Change_May_2013.pdf )

Thank you !

1. Christian,
I haven't tested it. I know it is Keenan's favorite. It's "Integrated" which makes it harder. But it also makes it unphysical, because it can drift without bound.

It seems to me that that is a fatal barrier. (3,1,0) noise can't be sustained in the long term. I've heard the counter - well, neither can a trend. But we have a basis for thinking the trend may have changed - we have put a who;e lot of GHG in the air. There's no reason to expect a new type of noise to suddenly come about.

It doesn't mean it is impossible. But I think Occam would prefer a model that doesn't involve such a postulate.