Thanks for the prompt response. I am calibrating the model to emerging countries data. When I use the built in IRF option at 3rd order. Then, we don’t observe greater initial response in consumption compare to output after positive TFP shocks which contradicts the second moments of models. That,s why I opted for IRF at EMAS.

Sorry, I could not get your point. What exactly I should look for?. I have given the codes below. Would you please refer to these codes to explain the problem as you mention.? Is there a way to confirm from the model whether Jensen’s Inequality holds or not? In our case 3rd derivative of utility(GHH) is positive. Which means a rise in uncertainty of about future income raise saving and decrease consumption. But this saving and consumption must depend on others parameters values such as relative risk aversion etc. **But Isn’t the case with IRFs at EMAS that household now expecting positive income in future(positive TFP) and they respond to it by raising consumption in beginning?**

```
sigmae = 0.0201;
rho = 0.86;
a = rho*a(-1)+ sigmae*e;
var e; stderr 1;
y_pos=strmatch('y',M_.endo_names,'exact');
c_pos=strmatch('c',M_.endo_names,'exact');
i_pos=strmatch('i',M_.endo_names,'exact');
h_pos=strmatch('h',M_.endo_names,'exact');
tb_y_pos=strmatch('tb',M_.endo_names,'exact');
ca_y_pos=strmatch('ca',M_.endo_names,'exact');
Z_pos=strmatch('a',M_.endo_names,'exact');
d_pos=strmatch('d',M_.endo_names,'exact');
lambda_pos=strmatch('lambda',M_.endo_names,'exact');
IRF_periods=50;
burnin=5000; %periods for convergence
shock_mat_with_zeros=zeros(burnin+IRF_periods,M_.exo_nbr); %shocks set to 0 to simulate without uncertainty
IRF_no_shock_mat = simult_(oo_.dr.ys,oo_.dr,shock_mat_with_zeros,options_.order)'; %simulate series
stochastic_steady_state=IRF_no_shock_mat(1+burnin,:); % stochastic_steady_state/EMAS is any of the final points after burnin
shock_mat = zeros(burnin+IRF_periods,M_.exo_nbr);
shock_mat(1+burnin,strmatch('e',M_.exo_names,'exact'))= 1;
IRF_mat = simult_(oo_.dr.ys,oo_.dr,shock_mat,options_.order)';
IRF_mat_percent_from_SSS = (IRF_mat(1+burnin+1:1+burnin+IRF_periods,:)-IRF_no_shock_mat(1+burnin+1:1+burnin+IRF_periods,:))./repmat(stochastic_steady_state,IRF_periods,1); %only valid for variables not yet logged
%scale IRFs as reqired
y_vola_IRF = 100*IRF_mat_percent_from_SSS(:,y_pos);
c_vola_IRF = 100*IRF_mat_percent_from_SSS(:,c_pos);
```