I am new at using Stan in R and want to know if what I am doing is possible or I am probably

replicating unnecessary code. My goal is to understand if there is a way to create a model that is a substitute

than running 1,000 times a stan_glm regression.

My data is as follows:

I have 1,000 different funds. Each fund has its own vector of returns (dependent variable), 240 values.

Each regression is ran against the same matrix of independent variables. I have a total of 3 independent

variables.

Using stan_glm it looks like this:

returns ← replicate(1000, rnorm(240,0,1)) #That way a have the 1,000 vectors with their `returns`

independenteVar ← replicate(3, rnorm(240,0,1))

stan_glm(returns[,i] ~ ., data = independentVar, refresh = 0) #I would use a loop and go through all the funds (i 1:1000)

So far here, I am running this 1,000 times (I do this as a Local Job, since it takes quite some time).

The idea of this is that I would have results for each fund based on their returns.

Now, is there a way to do this faster/more efficient with a proper Stan model.

In Stan I have this, but this is only for oen fund:

data {

int<lower=0> N; //Number of observations 240

int<lower=0> K; //Number of predictors, 3, all except the intercept

matrix[N, K] X; // predictor matrix

vector[N] y; // outcome vector

}

parameters {

real alpha; // intercept

vector[K] beta; // coefficients for predictors

real<lower=0> sigma; // error scale

}

model {

y ~ normal(alpha + X * beta, sigma); // target density

}

When I run this, it is only for one fund. I have to run it 1,000 times to have the 1,000 models. Somethins tells me this is not the correct way.

If more RC is necessary, I will try my best. As of now I do not care much about the priors, I am more interested in knowing how to do this.

Also, any reference to read, I will apreciate it. I felt that the Stan exampled do not solve my question, although,

I am probably wrong.

Thank you.