Hey. I am new to Stan and starting in the small. I have with success and by the help of the manual estimated a GARCH model on a sample hypothetical sample.

Now I am trying to model a GJR (threshold) GARCH, however, I get the error message:

[[1]] Stan model ‘rstan_GARCH_GJR’ does not contain samples.

I got i to work, had had chosen wrong pars (from the old model). The beneath code runs fine.

I hope You can help. My code is:

```
data {
int<lower=0> T; //number of observations
real r[T]; //return data
real<lower=0> sigma1; //First sigma
}
parameters {
real mu;
real<lower=0> omg; //Cannot be negative to ensure positive variance
real<lower=0,upper=1> alpha; //Cannot be negative to ensure positive variance
real<lower=0> psi; //Cannot be negative to ensure positive variance
real<lower=0> lam; //Cannot be negative to ensure positive variance
}
transformed parameters{
real<lower=0> sigma[T];
real<lower=0, upper=(1-alpha-psi/2)> bet; //Upper limit to ensure stationarity
sigma[1] = sigma1;
bet = lam - alpha - psi/2 ;
for (t in 2:T)
sigma[t] = sqrt(omg + (alpha + psi*(step(r[t - 1] - mu)-1)*-1)*pow(r[t - 1] - mu,2) + bet*pow(sigma[t-1],2));
}
model {
//Data distribution / model
r~normal(mu, sigma);
//prior distributions
omg~beta(2, 10);
alpha~beta(2, 20);
psi~beta(2,20);
lam~beta(25, 1.5);
mu~normal(0, 2);}
```

Which is called from R using Rstan:

```
### test
library(ggplot2)
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
y <- list(r = c(0.02, 0.03, 0.05, 0.02, 0.01, 0.02, 0.1), T = 7, sigma1 = 0.01, T_out = 8)
fit <- rstan::stan(file = "/rstan_GARCH_GJR.stan",
data = y, iter = 1000, chains = 1, cores = 4, pars = c("mu", "omg", "psi", "lam", "alpha")))
```

[edit: escaped code]