Mashinali o‘qitishga kirish Nosirov Xabibullo xikmatullo o‘gli Falsafa doktori (PhD), tret kafedrasi mudiri

Load the sample data.

load('flu')

The dataset array flu contains national CDC flu estimates, and nine separate regional estimates based on Google® query data.

Extract the response and predictor data.

Y = double(flu(:,2:end-1)); [n,d] = size(Y); x = flu.WtdILI;

The responses in Y are the nine regional flu estimates. Observations exist for every week over a one-year period, so n = 52. The dimension of the responses corresponds to the regions, so d = 9. The predictors in x are the weekly national flu estimates.

Multivariate regression model for panel data with different intercepts

Plot the flu data, grouped by region.

figure; regions = flu.Properties.VarNames(2:end-1); plot(x,Y,'x') legend(regions,'Location','NorthWest')

Multivariate regression model for panel data with different intercepts

Fit the multivariate regression model yij=αj+βxij+ϵij, where i=1,…,n and j=1,…,d, with between-region concurrent correlation COV(ϵij,ϵij)=σjj.