I am looking for an urgent help in this problem. I will be indebted for helping me out in this problem:

I am trying to build a time varying VAR model in stan:

1- I modelled the constants+ coefficients as random walk.

2- I modelled the covariances as stochastic volatility random walk.

But I failed to find a way to model the covariances between the variables as random walk. Here is the stan model:

```
int<lower=1> T;
int<lower=1> N;
int<lower=1> CC;
matrix[T, N] Y;
matrix[N, N*(N+1)] X[T-1];
}
parameters {
vector[CC] B_raw[T-1];
vector[CC] B1;
vector<lower=-1, upper=1>[CC] b;
real<lower=0> tau;
vector[N] z_sigma[T-1];
vector[N] Z1;
real<lower=0> tau_sig;
real<lower=-1, upper=1> s;
real<lower=-1, upper=1> rho_raw[T-1];
real<lower=0> tau_rho;
}
transformed parameters {
vector[CC] B[T-1];
vector[N] log_sigma[T-1];
real<lower=-1, upper=1> rho[T-1];
B[1,1] = B1[1];
B[1,2] = B1[2];
B[1,3] = B1[3];
B[1,4] = B1[4];
B[1,5] = B1[5];
B[1,6] = B1[6];
log_sigma[1,1] = Z1[1];
log_sigma[1,2] = Z1[2];
rho[1] = 0;
for (t in 2:(T-1)) {
for (c in 1:CC){
B[t] = b[c] * B[t-1] + tau * B_raw[t];
}
for (n in 1:N){
log_sigma[t] = s * log_sigma[t-1] + tau_sig * z_sigma[t];
}
rho[t] = rho[t-1] + tau_rho * rho_raw[t];
}
}
model {
vector[N] Mean;
B1 ~ normal(0.1, 0.5);
Z1 ~ normal(-3, 1);
b[1]~ normal(0.5, 1);
b[2]~ normal(0.5, 1);
s ~ normal(0.5, 1);
for(n in 1:CC) {
for (t in 1:T-1) {
B_raw[t, n] ~ normal(0, 1);
}
}
for(n in 1:N) {
for (t in 1:T-1) {
z_sigma[t, n] ~ normal(0, 1);
}
}
for (t in 1:T-1) {
rho_raw[t] ~ normal(0, 1);
}
tau ~ normal(0, 0.3);
tau_sig ~ normal(0, 0.3);
tau_rho ~ normal(0, 0.3);
for (t in 2:T) {
Mean = X[t-1] * B[t-1];
matrix[2, 2] Sigma;
Sigma[1,1] = exp(2 * log_sigma[t-1, 1]);
Sigma[2,2] = exp(2 * log_sigma[t-1, 2]);
Sigma[1,2] = rho[t-1] * exp(log_sigma[t-1, 1]) * exp(log_sigma[t-1, 2]);
Sigma[2,1] = Sigma[1,2];
Y[t] ~ multi_normal(Mean, Sigma);
}
}```
```