Compute the p-value of Diebold-Mariano Test
15 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Astrik
am 22 Jan. 2017
Kommentiert: yl x
am 1 Nov. 2021
I am using Diebold-Mariano Test for testing the equal predictive accuracy. The use the code written by Semin Ibisevic (2011) to compute it
function DM = dmtest(e1, e2, h)
% Initialization
T = size(e1,1);
% Define the loss differential
d = e1.^2 - e2.^2;
% Ralculate the variance of the loss differential, taking into account
% autocorrelation.
dMean = mean(d);
gamma0 = var(d);
if h > 1
gamma = zeros(h-1,1);
for i = 1:h-1
sampleCov = cov(d(1+i:T),d(1:T-i));
gamma(i) = sampleCov(2);
end
varD = gamma0 + 2*sum(gamma);
else
varD = gamma0;
end
% Retrieve the diebold mariano statistic DM ~N(0,1)
DM = dMean / sqrt ( (1/T)*varD );
Now, as we see the DM statistic is standard normally distributed.My question is how can I calculate the p-value of this statistic? The null hypothesis is rejected every time the DM is outside the range [-1,96 1,96]
Akzeptierte Antwort
Star Strider
am 22 Jan. 2017
If it’s normally distributed, you can use either:
P = @(z) erfc(-z/sqrt(2))/2; % Equivalent to ‘normcdf’
or if you have the Statistics and Machine Learning Toolbox, the normcdf function to compute the p-value.
However, it’s probably easier to compare it to the ±1.96 criteria and be done with it.
0 Kommentare
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Hypothesis Tests finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!