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(This question is a crosspost from Cross Validated)

I have a following stochastic model describing evolution of a process (Y) in space and time. Ds and Dt are domain in space (2D with x and y axes) and time (1D with t axis). This model is usually known as mixed-effects model or components-of-variation models

enter image description here

I am currently developing Y as follow:

%# Time parameters
T=1:1:20; % input

%# Grid and model parameters

[Grid.Nx,Grid.Ny,Grid.Nt] = meshgrid(1:1:nCol,1:1:nRow,T);


deterministic_mu = detConstant.*(((Grid.Nt).^tPower)./((Grid.Nx).^xPower));

beta_s = randn(nRow,nCol); % mean-zero random effect representing location specific variability common to all times

gammaTemp = randn(nT,1);

for t = 1:nT
    gamma_t(:,:,t) = repmat(gammaTemp(t),nRow,nCol); % mean-zero random effect representing time specific variability common to all locations

var=0.1;% noise has variance = 0.1
for t=1:nT
    kappa_st(:,:,t) = sqrt(var)*randn(nRow,nCol);

for t=1:nT
    Y(:,:,t) = deterministic_mu(:,:,t) + beta_s + gamma_t(:,:,t) + kappa_st(:,:,t);

Can someone help explain, through some illustration using Matlab, if I am correctly producing Y? Also, how to produce delta in the expression for Y?

Please let me know if you need some more information/explanation. Thanks.

share|improve this question
It would be nice if you provide some information regarding application of this model to finance. – Alexey Kalmykov Sep 26 '12 at 19:47
I am not sure if this has an exact application in finance, but the underlying stochastic modeling is also used in quant finance. Purposefully, I have asked the question based on concept than on application which could be many, including finance. – Pupil Sep 26 '12 at 20:00

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