Compute the p-value of Diebold-Mariano Test

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]

[yl x]

if h =1 ,so what it is

Answer 1

[Star Strider]

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.