Hello Everyone,

I am Dapomi from the University of Ibadan, Nigeria. I am currently working on ‘The of the Interest on Business Cycle in Nigeria’. I am trying to use the DSGE Model using Dynare code on Matlab. However, I am currently try my hand the following code:

var y c k i l y_l w r z;

varexo e;

parameters beta psi delta alpha rho sigma epsilon;

alpha = 0.33;

beta = 0.99;

delta = 0.023;

psi = 1.75;

rho = 0.95;

sigma = (0.007/(1-alpha));

epsilon = 10;

model;

(1/c) = beta*(1/c(+1))*(1+r(+1)-delta);
psi*c/(1-l) = w;

c+i = y;

y = (k(-1)^alpha)

*(exp(z)*(1-alpha)/l;

*l)^(1-alpha);*

w = y((epsilon-1)/epsilon)w = y

r = y*((epsilon-1)/epsilon)*alpha/k(-1);

i = k-(1-delta)

*k(-1);*

y_l = y/l;

z = rhoz(-1)+e;

y_l = y/l;

z = rho

end;

initval;

k = 9;

c = 0.76;

l = 0.3;

w = 2.07;

r = 0.03;

z = 0;

e = 0;

end;

steady;

check;

shocks;

var e = sigma^2;

end;

varobs y;

estimated_params;

beta, beta_pdf, 0.99, 0.02;

stderr e, inv_gamma_pdf, 0.01, inf;

end;

estimation(datafile=foreview,mode_compute=0,mode_file=dsgeNine_mode,mh_replic=2000,mh_nblocks=2,mh_drop=0.5,mh_jscale=0.2);

The excel data of the Nigerian GDP I am using for this model is attached with this topic/message.

However, after running the model the following is the result that I am getting with errors in it that I cannot interprete and unable to get my posterior mean:

Configuring Dynare …

[mex] Generalized QZ.

[mex] Sylvester equation solution.

[mex] Kronecker products.

[mex] Sparse kronecker products.

[mex] Local state space iteration (second order).

[mex] Bytecode evaluation.

[mex] k-order perturbation solver.

[mex] k-order solution simulation.

[mex] Quasi Monte-Carlo sequence (Sobol).

[mex] Markov Switching SBVAR.

Starting Dynare (version 4.4.3).

Starting preprocessing of the model file …

Found 9 equation(s).

Evaluating expressions…done

Computing static model derivatives:

- order 1

Computing dynamic model derivatives: - order 1
- order 2

Processing outputs …done

Preprocessing completed.

Starting MATLAB/Octave computing.

STEADY-STATE RESULTS:

y 0.892084

c 0.707986

k 8.00425

i 0.184098

l 0.302733

y_l 2.94677

w 1.7769

r 0.033101

z 0

EIGENVALUES:

Modulus Real Imaginary

```
0.9498 0.9498 0
0.95 0.95 0
1.071 1.071 0
Inf Inf 0
```

There are 2 eigenvalue(s) larger than 1 in modulus

for 2 forward-looking variable(s)

The rank condition is verified.

You did not declare endogenous variables after the estimation/calib_smoother command.

Loading 53 observations from foreview.xlsx

Initial value of the log posterior (or likelihood): -26085.2619

POSTERIOR KERNEL OPTIMIZATION PROBLEM!

(minus) the hessian matrix at the “mode” is not positive definite!

=> posterior variance of the estimated parameters are not positive.

You should try to change the initial values of the parameters using

the estimated_params_init block, or use another optimization routine.

Warning: The results below are most likely wrong!

In dynare_estimation_1 at 694

In dynare_estimation at 89

In dsgeNine at 178

In dynare at 180

RESULTS FROM POSTERIOR ESTIMATION

parameters

prior mean mode s.d. prior pstdev

beta 0.990 1.0000 0.0000 beta 0.0200

standard deviation of shocks

prior mean mode s.d. prior pstdev

e 0.010 1.5659 0.0000 invg Inf

Log data density [Laplace approximation] is -26098.508064.

??? Error using ==> chol

Matrix must be positive definite.

Error in ==> metropolis_hastings_initialization at 68

d = chol(vv);

Error in ==> random_walk_metropolis_hastings at 62

ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile,

MAX_nruns, d ] = …

Error in ==> dynare_estimation_1 at 782

feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);

Error in ==> dynare_estimation at 89

dynare_estimation_1(var_list,dname);

Error in ==> dsgeNine at 178

dynare_estimation(var_list_);

Error in ==> dynare at 180

evalin(‘base’,fname) ;

I will be so glad if you could help on what to do. Besides, I am using Matlab2009. Thanks so much as I await your helpful responses.

NB: This is the data I used

y

514.83

505.40

515.26

547.71

562.66

577.59

541.21

446.16

430.97

523.36

639.40

713.63

720.39

740.91

802.67

740.26

784.23

806.83

737.50

764.19

773.86

654.23

635.90

587.13

544.86

582.59

581.86

562.79

602.42

629.12

663.33

677.39

679.78

677.54

661.49

661.32

672.75

673.86

669.56

660.18

678.59

682.25

675.56

726.45

783.07

804.15

831.79

862.14

889.43

925.79

972.55

1015.56

1052.34

Kind regards,

Dapomi.