# Four ways to compute the steady state of a DSGE model

In these tutorials, we will discuss the different ways to compute the steady state for the models discussed here.

## RBC model





Note that when we consider the log-utility case, the steady state is given analytically, whereas in the CES utility case we cannot solve for labor in closed-form.

Let’s consider the log-utility case, i.e. we have a recipe to compute the steady state in closed-form analytically. This can be easily written down in a steady_state_model block in your mod file:

# rbc_logutil.mod
var Y C K L A R W I;
varexo eps_A;
parameters alph betta delt gam pssi rhoA;

alph = 0.35; betta = 0.99; delt = 0.025; gam = 1; pssi = 1.6; rhoA = 0.9;

model;
#UC  = gam*C^(-1);
#UCp = gam*C(+1)^(-1);
#UL  = -pssi*(1-L)^(-1);
UC = betta*UCp*(1-delt+R);
W = -UL/UC;
K = (1-delt)*K(-1)+I;
Y = I+C;
Y = A*K(-1)^alph*L^(1-alph);
W = (1-alph)*Y/L;
R = alph*Y/K(-1);
log(A) = rhoA*log(A(-1))+eps_A;
end;

A = 1;
R = 1/betta+delt-1;
K_L = ((alph*A)/R)^(1/(1-alph));
W = (1-alph)*A*K_L^alph;
I_L = delt*K_L;
Y_L = A*K_L^alph;
C_L = Y_L-I_L;

% closed-form expression for labor when using log utility
L = gam/pssi*C_L^(-1)*W/(1+gam/pssi*C_L^(-1)*W);

C = C_L*L;
I = I_L*L;
K = K_L*L;
Y = Y_L*L;
end;

resid;


This is the preferred method to specify your steady state in your mod file, but it takes much time, effort, and practice to derive the closed-form solutions.

### Method 2: steady_state_model with helper function

Often you can derive closed-form expressions for some variables in steady state, but not for a few others. An example of this is the RBC model with CES utility, where we cannot solve for labor analytically, but once we have a value for labor, all other variables can be computed in closed-form. Such cases can be coped with by using a helper function in your steady_state_model block. Consider the following mod file first:

# rbc_ces1.mod
var Y C K L A R W I;
varexo eps_A;
parameters alph betta delt gam pssi rhoA etaC etaL;

alph = 0.35; betta = 0.99; delt = 0.025; gam = 1; pssi = 1.6; rhoA = 0.9;
etaC  = 2;   etaL  = 1.5;

model;
#UC  = gam*C^(-etaC);
#UCp = gam*C(+1)^(-etaC);
#UL  = -pssi*(1-L)^(-etaL);
UC = betta*UCp*(1-delt+R);
W = -UL/UC;
K = (1-delt)*K(-1)+I;
Y = I+C;
Y = A*K(-1)^alph*L^(1-alph);
W = (1-alph)*Y/L;
R = alph*Y/K(-1);
log(A) = rhoA*log(A(-1))+eps_A;
end;

A = 1;
R = 1/betta+delt-1;
K_L = ((alph*A)/R)^(1/(1-alph));
W = (1-alph)*A*K_L^alph;
I_L = delt*K_L;
Y_L = A*K_L^alph;
C_L = Y_L-I_L;

% closed-form expression for labor is not possible, so we need a helper function
L0 = 1/3;

C = C_L*L;
I = I_L*L;
K = K_L*L;
Y = Y_L*L;
end;

resid;


Note that in the steady_state_model block we are calling the helper function rbc_ces1_steadystate_helper.m, which uses MATLAB to compute L given a start value L0 and previously computed steady state values. You need to create this file (and call it whatever you want it to be called) in the same folder as your mod file, but use the .m extension. I called it rbc_ces1_steadystate_helper.m:

function L = rbc_ces1_steadystate_helper(L0,pssi,etaL,etaC,gam,C_L,W)
if etaC == 1 && etaL == 1
L = gam/pssi*C_L^(-1)*W/(1+gam/pssi*C_L^(-1)*W);
else
options = optimset('Display','Final','TolX',1e-10,'TolFun',1e-10);
L = fsolve(@(L) pssi*(1-L)^(-etaL)*L^etaC - gam*C_L^(-etaC)*W, L0,options);
end
end


Note that I use fsolve to compute the labor value in steady state and set up some options using optimset. You can and should, of course, use other optimizers that work better in your model. Also, note that I catch the special case, where there is the closed-form solution.

If you want full control of the steady state computations, you need to create your own YOURMODFILENAME_steadystate.m. So let’s create another mod file

# rbc_ces2.mod
var Y C K L A R W I;
varexo eps_A;
parameters alph betta delt gam pssi rhoA etaC etaL;

alph = 0.35; betta = 0.99; delt = 0.025; gam = 1; pssi = 1.6; rhoA = 0.9;
etaC  = 2;   etaL  = 1.5;

model;
#UC  = gam*C^(-etaC);
#UCp = gam*C(+1)^(-etaC);
#UL  = -pssi*(1-L)^(-etaL);
UC = betta*UCp*(1-delt+R);
W = -UL/UC;
K = (1-delt)*K(-1)+I;
Y = I+C;
Y = A*K(-1)^alph*L^(1-alph);
W = (1-alph)*Y/L;
R = alph*Y/K(-1);
log(A) = rhoA*log(A(-1))+eps_A;
end;

resid;


Note that there is no steady_state_model or initval block, but the sole command steady which instructs Dynare to compute the steady state. Now let’s create another MATLAB file, called rbc_ces2_steadystate.m, which actually computes the steady state:

function [ys,params,check] = rbc_ces2_steadystate(ys,exo,M_,options_)
% Inputs:
%   - ys        [vector] vector of initial values for the steady state of the endogenous variables
%   - exo       [vector] vector of values for the exogenous variables
%   - M_        [structure] Dynare model structure
%   - options   [structure] Dynare options structure
%
% Output:
%   - ys        [vector] vector of steady state values for the the endogenous variables
%   - params    [vector] vector of parameter values
%   - check     [scalar] 0 if steady state computation worked and to
%                        1 of not (allows to impose restrictions on parameters)

%% Step 0: initialize indicator and set options for numerical solver
check = 0;
options = optimset('Display','Final','TolX',1e-10,'TolFun',1e-10);

%% Step 1: read out parameters to access them with their name
for ii = 1:M_.param_nbr
eval([ M_.param_names{ii} ' = M_.params(' int2str(ii) ');']);
end

%% Step 2: Check parameter restrictions
if etaC*etaL<1 % parameter violates restriction (here it is artifical)
check=1; %set failure indicator
return;  %return without updating steady states
end

%% Step 3: Enter model equations here
A = 1;
R = 1/betta+delt-1;
K_L = ((alph*A)/R)^(1/(1-alph));
if K_L <= 0
check = 1; % set failure indicator
return;    % return without updating steady states
end

W = (1-alph)*A*K_L^alph;
I_L = delt*K_L;
Y_L = A*K_L^alph;
C_L = Y_L-I_L;

if C_L <= 0
check = 1; % set failure indicator
return;    % return without updating steady states
end

% The labor level
if etaC == 1 && etaL == 1
% Closed-form solution for labor
L = gam/pssi*C_L^(-1)*W/(1+gam/pssi*C_L^(-1)*W);
else
% No closed-form solution use a fixed-point algorithm
L0 = 1/3;
[L,~,exitflag] = fsolve(@(L) pssi*(1-L)^(-etaL)*L^etaC - gam*C_L^(-etaC)*W, L0,options);
if exitflag <= 0
check = 1; % set failure indicator
return     % return without updating steady states
end
end

C = C_L*L; % consumption level
I = I_L*L; % investment level
K = K_L*L; % capital level
Y = Y_L*L; % output level

%% Step 4: Update parameters and variables
params=NaN(M_.param_nbr,1);
for iter = 1:M_.param_nbr %update parameters set in the file
eval([ 'params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
end

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


Some remarks are in order:

1. We usually do not recommend to write you own file to compute the steady state as this is error-prone. But a definite advantage is that you have full control of the computations.
2. Your _steadystate.m file needs to have the same first part of the name as your mod file.
3. Step 1 and Step 4 are always the same and very important to include in your steadystate file.
4. Note that previous to Dynare 4.6 M_ and options_ were called globally, but not anymore. Thus, we need to use these variables as inputs. This is a new feature as previously people used the global variables in a wrong fashion, e.g. falsely updated them in an estimation exercise. So if you have older mod files, you need to adapt this accordingly.
5. Before I actually call the numerical optimizer, I included examples on how to check parameter and variable restrictions. If they are not fulfilled, the function returns a flag that the steady state could not be computed. This is good practice as the numerical solver could be time-consuming or would lead unfeasible results.
6. Lastly, you need to specify values for all your declared variables and output both parameters and variables. Dynare then updates the auxiliary variables internally.

### Method 4: initval

If you have no clue at all on how to compute the steady state of your model, you can always rely on numerical methods. Dynare has several in-built optimization algorithms you can choose and fine-tune (see the manual on all available options). You need to specify an initval block with the initial values for the endogenous variables:

var Y C K L A R W I;
varexo eps_A;
parameters alph betta delt gam pssi rhoA etaC etaL;

alph = 0.35; betta = 0.99; delt = 0.025; gam = 1; pssi = 1.6; rhoA = 0.9;
etaC  = 2;   etaL  = 1.5;

model;
#UC  = gam*C^(-etaC);
#UCp = gam*C(+1)^(-etaC);
#UL  = -pssi*(1-L)^(-etaL);
UC = betta*UCp*(1-delt+R);
W = -UL/UC;
K = (1-delt)*K(-1)+I;
Y = I+C;
Y = A*K(-1)^alph*L^(1-alph);
W = (1-alph)*Y/L;
R = alph*Y/K(-1);
log(A) = rhoA*log(A(-1))+eps_A;
end;

initval;
Y = 1.2;
C = 0.9;
K = 12;
L = 0.35;
A = 1;
R = 0.03;
W = 2.24;
I = 0.3;
end;