Hi, I´m trying to run the following script

library(tidyr)
library(dplyr)
library(bayesGARCH)
library(coda)
library(ggplot2)
library(patchwork)
library(quantmod)

CORN_futures <- getSymbols("ZC=F", auto.assign = FALSE,
from = "2021-08-01", end = Sys.Date()) # Target

log_ret <- diff(log(Cl(CORN_futures)))

log_ret <- log_ret[1:182,] %>% na.exclude()

sum(is.na(log_ret))

log_ret <- log_ret[2:180]

set.seed(1234)

MCMC <- bayesGARCH(log_ret, control = list(l.chain = 5000, n.chain = 2))

Error in uniroot(fn.neg.alpha, interval = c(alpha.min, alpha.max), tol = .Machine$double.eps, : f() values at end points not of
opposite sign Calls: ... bayesGARCH -> fn.bayesGARCH -> fn.block -> fn.nu.full -> unitroot

How can I fix it ?

Hi @ArdiaD,

thanks for the package. For improved MCMC diagnostics for my case study, I wanted to run multiple chains and also provide starting values. I got into an error that I was able to overcome only by reading the source code of the package.

MWE:

model_control <- list(n.chain = 2,
                      start.val = matrix(c(0.01, 0.1, 0.7, 500,
                                           0.3, 3, 0.01, 500),
                                         2, 4, byrow = TRUE))
fit <- bayesGARCH(rep(0, 10), control = model_control)

The following error is thrown before sampling starts:

Error in dimnames(x) <- dn : 
  length of 'dimnames' [2] not equal to array extent

The problem is in this line:

It is true that providing n.chain is redundant if start.val is given but I personally found the behavior counterintuitive (I think in JAGS and Stan you have to provide both) and the solution should be simple.
I hope you find this ticket useful.

Cheers,
Darjus