regression - Matlab multivarible regresison with time dependent variables -


i'm trying develop code find significance of using auxiliary data source improve predictability of final product. have data ready in matlab, preferred program analysis.

i'm trying solve following equation.

p(t,i) = a(i) + b(i)*z(t,i) + c(i)*y(t,i) + d(i)*x(t,i) + e(i)*w(i)

where, p, z, y, x, w known, t , indices , wish find values a, b, c, d , e minimise difference between existing value of p , predicted value of p.

t = 1:20 , ~ 1:250000

eventually set value of e(i) 0 , see how improvement adding variable, before testing random number stream too.

if more detail needed try provide it, many thanks.

i've tried method suggested below because z, y , x values matrices output matrix sol 3 times width of t + 1 element of e. i've read further around , think method should 1 of either generalised linear model or panel regression model i'm not sure how set 1 up. i've re-read examples mathworks few times , still confused.

you can calculate coefficients using mldivide, matlab give least-squares solution overdetermined system. if understand question correctly, want calculate coefficients every i, have iterate on i.

in code, (untested):

for i=1:250000   m = [ones(size(p(:,i))), z(:,i), y(:,i), x(:,i), w(:,i)];   sol = m\p(:,i);    a(i) = sol(1);   b(i) = sol(2);   c(i) = sol(3);   d(i) = sol(4);   e(i) = sol(5); end

you can find further information in documentation.


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