function [ys,check] = Sentiment_steadystate(ys,exo)
% function [ys,check] = Sentiment_steadystate(ys,exo)
% computes the steady state for the Sentiment.mod and uses a numerical
% solver to do so
% Inputs: 
%   - ys        [vector] vector of initial values for the steady state of
%                   the endogenous variables
%   - exo       [vector] vector of values for the exogenous variables
%
% Output: 
%   - ys        [vector] vector of steady state values fpr the the endogenous variables
%   - check     [scalar] set to 0 if steady state computation worked and to
%                    1 of not (allows to impos restriction on parameters)

global M_ 

% read out parameters to access them with their name
NumberOfParameters = M_.param_nbr;
for ii = 1:NumberOfParameters
  paramname = deblank(M_.param_names(ii,:));
  eval([ paramname ' = M_.params(' int2str(ii) ');']);
end
% initialize indicator
check = 0;


%% Enter model equations here

options=optimset(); % set options for numerical solver

% the steady state computation follows 
PI=1.15;
A=1;
or=1;
g=1;
PIstar=1;
nu=1;
mb=PI;
i=(PI/betta)-1;
r=(1/betta)-(1-delta);
mc=(phi*(PI^(1-(chi*mu1))-1)*PI^(1-(chi*mu1))*(1-betta)+theta-1)/theta;
w=(1-alppha)*(mc*(alppha/r)^alppha)^(1/(1-alppha));
c=w/psin;
m=psim*c*(1+i)/i;

k=(c+1)/((((1-alppha)*r)/(alppha*w))^(1-alppha)*(1-(phi/2)*(PI^(1-(chi*mu1))-1)^2)-delta);

n=((1-alppha)*r*k)/(alppha*w);
x=delta*k;
y=(n^(1-alppha))*(k^alppha);
fr=omega*(PI/(PI-1));
ndf=(PI/(PI-1))*(1-omega);
dc=m-fr;





%% end own model equations

for iter = 1:length(M_.params) %update parameters set in the file
  eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
end

NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
for ii = 1:NumberOfEndogenousVariables
  varname = deblank(M_.endo_names(ii,:));
  eval(['ys(' int2str(ii) ') = ' varname ';']);
end
