📄 arimalik.m
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function [l , g] = arimalik(phitheta,z,p,d)% ARIMALIK(Z,PHI,THETA,D) (concentrated) -2 log likelihood of ARIMA model% INPUTS:% z = data vector; n by 1; missing values allowed% phitheta = vector of AR coeffs. concatenated with MA coeffs. (p+q by 1)% p = order of AR% d = order of differencing% RETURNS: l = n log (x-muhat)^T R^{-1} (x-muhat) + log |R|% where n = length(z)-d% x = d-fold differenced process% R = correlation matrix corresponding to phi, theta% muhat = generalized least squares estimate of mean mu% Except for constant terms, l is -2 log likelihood% g = 2 by 1 vector of [ r1 ; r2] where r1 is 1 - minimum modulus of% roots of AR equation and r2 is for MA equation.phi = phitheta(1:p);[m n] = size(phitheta);theta = phitheta((p+1):m);x = z;if d > 0 for k=1:d x = diff(x); endend[n m] = size(x);acf = armaacf(phi,theta,n-1);R = toeplitz(acf);T = chol(R);dt = prod(diag(T))^2;y = backsolve(T,[x ones(n,1)],1,1);muhat = (y(:,1)'*y(:,2))/(y(:,2)'*y(:,2));y1 = y(:,1)-muhat*y(:,2);s2 = y1'*y1/n;l = n*log(s2) + log(dt);g = [ statcheck(phi) ; statcheck(theta) ];
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