Diebold Li (2006) AR process
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Hi there,
I have a question about the paper by Diebold and Li (2006). They estimated a time-series of three factors subsequently they want to forecast these parameters to forecast the yield curve. I have exactly the same estimated factors, however, when I want to forecast the factors I get different results. They say they model the factor B[t+h] = c + y*B[t] by a simple regression. However, when you perform this regression on simply the previous B, you will only get an estimate of for y and the constant c is always zero right?
Here is the paper
Kind regards,
Michael
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Answers (1)
the cyclist
on 28 Jun 2014
Why would c be zero? Wouldn't the autoregression of B be something like
B = [1 1 2 3 5 8 13 21 34]';
regressCoeffs = regress(B(2:end)',[ones(8,1) B(1:end-1)']);
c = regressCoeffs(1);
y = regressCoeffs(2);
where I have obviously just put in some nonsense data.
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the cyclist
on 29 Jun 2014
You need to estimate not just the relationship of the last observation to the next-to-last, but rather all observations (except the first one) to the one just prior. So, you are doing the estimate of the coefficients c and y that best fit
B(2) = c + y*B(1)
B(3) = c + y*B(2)
B(4) = c + y*B(3)
etc.
Or maybe I misunderstand.
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