[Parameters of the model]
a=1.0050     
alpha=0.73   
beta=0.994   
delta=0.011  
eta=1.0      
nstar=0.13
nu=2.0
gam=10.0
rho=0.90
sigma=0.0072

[Parameter that controls the non-linear equations solver]
jac=3 !1=forward difference formula from IMSL library, 2=our own forward difference formula, 3=central difference formula

[Parameter that determines the utility function, can be 1, 2, 3 or 4]
utility=4

[Parameter that determines the solution method, 1=VI, 2=EDP, 3=PEA, 4=PJ, 5=LA, 6=Second Order Approximation, 7=summary statistic]
method=6

[Parameters that determine the computation of the DM statistic]
dm_stat=.true. ! .false. do not compute this statistic
dm_nobs=3000   ! number of point in the time series from which dm is computed
dm_nofs=500    ! number of simulations used to compute dm
dm_start=51    ! first observation that counts in the computation of the dm statistic

[Euler]
compute_eu=.true. ! if true, compute Euler equation residuals
eu_kmin=0.8 ! eu_kmin*kstar is the lower bound of the interval 
eu_kmax=1.2 ! eu_kmax*ksatr is the upper bound of the interval
eu_zmin=0.9 ! eu_zmin*1 is the lower bound of the interval
eu_zmax=1.1 ! eu_zmax*1 is the upper bound of the interval
eu_nz=20    ! number of points in the interval for z for which the residual is evaluated
eu_nk=20    ! number of points in the interval for k for which the residual is evaluated

[RRR]
compute_rrr=.true. ! if true, compute risk free rate of return
rrr_nobs=100000 ! length of time series from which the average risk free rate of return is computed

[Parameters that determine the computation of the Euler residuals]
eu_nz=10     ! number of points z in [zmin,zmax] at which the Euler residual is computed

[Parameters that determine the computation of moments, hpl=filer weigth for HP filter, nofs: number of simulations of length nobs]
compute_moments=.true. ! if true, simulate the model and compute second moments
hpl=1600               ! filter weight of the HP-filter applied to simulated time series
nobs=60                ! length of each time series
nofs=500               ! number of simulations   

[computation of the policy function]
policy=.true.    ! if true, compute consumption as a function of k,z
z_index=10       ! the index of z in zgrid at which the policy function is computed


[Parameters that are specific to certain methods]
[VI]
vi_method=1        !1=value function iteration, 2=value function iteration with continuous markov process, 3=policy function iteration using Howard's mehtod
vi_markov=1        !1=use discrete markov chain in simulation, 2=use continous markov process in simulation
vi_initial=.false. ! if true: the policy function stored in vi_policy is loaded
ngrid=5000         ! size of grid of the stock of capital
stopafter=30       ! stop if after .. concecutive iterations the policy function has not changed
epsilon=0.01       ! stop if v^{s+1}-v^s <= (1-beta)*epsilon
vi_size=1.5        ! 2*vi_size times the standard deviation of Z is the size of the grid for Z
vi_nz=9            ! the number of point in the grid for Z
vi_kmin=0.9        ! vi_kmin*stationary stock of capital at min(z) is the lower bound of the grid of k
vi_kmax=1.1        ! vi_kmax*starionary stock of capital at max(z) is the upper bound of the grid of k

[EDP]
tend=150           ! time needed to approach stationary solution
nstep=10           ! number of homotopy steps to find a solution beginning from a known one

[PEA]
pea_nobs=50000        ! length of time series upon which the solution is based
pea_initial_z=.true.  ! if true, draw a new series of shocks otherwise load shocks from eps.bin
pea_initial_p=.true.  ! if true, load intial values from pea_parini.bin 
tstart=501            ! observations to discard in the computation of the solution

[Projection]
pj_initial=.false.    ! if true, load initial values from pj_parini.bin
pj_la=.true.          ! if true, use the solution from the log-linear method to initialize the coefficients of the polynomial
pj_increase_1=.false. ! if true, increase degree for z by one (the file pj_parini.bin must thus have (degree1-1)*degree2 elements!)
pj_increase_2=.false. ! if true, increase degree for k by one (the file pj_parini.bin must thus have degree1*(degree2-1) elementa!) 
pj_constraint=.true.  ! if true, implement constraint on investment
pj_size=4.5           ! 2*pj_size is the size of the interval over which the algorithm integrates to obtain the condition expectations
search=1              ! use algorithm Search1 or Search2 to find starting values for PEA and PJ
degree1=3             ! degree of the polynomial for ln Z
degree2=3             ! degree of the polynomial in  ln k
kmin_psi=0.7          ! kmin_psi*kstar is the lower bound on k used in the polynomial that approximates the solution
kmax_psi=1.5          ! kmax_psi*kstar is the upper bound on k used in the polynomial that approximates the solution
kmin_int=0.8          ! kmin_int*kstar is the lower bound of integration
kmax_int=1.2          ! kmax_int*kstar is the upper bound of integration
znodes=10             ! integration nodes for z
knodes=50             ! integration nodes for k

[Name of output file]
outfile=HM1.txt