function [outvalue, prior_mean, prior_variance] =compute_prior_weights(theta)
% function [outvalue, prior_mean, prior_variance] =compute_prior_weights(theta)
% Compute the prior density of the parameters; uses only first and second moments, i.e.
% uses a normal distribution
%
% Inputs:
%   - theta         [vector]    vector of parameters
%
% Outputs:
%   - outvalue          [scalar]    prior density
%   - prior_mean        [vector]    mean of the density function
%   - prior_variance    [vector]    covariance of the density function
%
% written by Johannes Pfeifer and Benjamin Born

%prior mean
prior_mean(1,1)=0.7;% phi_c
prior_mean(2,1)=0.15; %delta_2divdelta_1
prior_mean(3,1)=0.5; %rho_R
prior_mean(4,1)=0.5; %theta_p
prior_mean(5,1)=0.5; %theta_w
prior_mean(6,1)=0.5; %chi_p
prior_mean(7,1)=0.5; %chi_w
prior_mean(8,1)=1; %sigma_l
prior_mean(9,1)=1.5; %phi_Rpi
prior_mean(10,1)=0.5; %phi_Ry
prior_mean(11,1)=9; %gammma
prior_mean(12,1)=7; %kappa
prior_mean(13,1)=log(0.64/100/8); %sigma_ME

%prior_variance
prior_variance(1,1)=2; %phi_c
prior_variance(2,2)=4; %delta_2divdelta_1
prior_variance(3,3)=0.5; %rho_R
prior_variance(4,4)=0.4; %theta_p
prior_variance(5,5)=0.4; %theta_w
prior_variance(6,6)=2; %chi_p
prior_variance(7,7)=2; %chi_w
prior_variance(8,8)=1; %sigma_l
prior_variance(9,9)=6; %phi_Rpi
prior_variance(10,10)=0.5; %phi_Ry
prior_variance(11,11)=Inf; %gammma
prior_variance(12,12)=40; %kappa
prior_variance(13,13)=1; %sigma_ME (25% of wage standard deviation)

warning('off','MATLAB:singularMatrix') %required due to one Infinite variance
outvalue=(theta-prior_mean)'/prior_variance*(theta-prior_mean);