FitML | R Documentation |
Method that performs Maximum Likelihood estimation
of a MSGARCH_SPEC
object on a set of observations.
FitML(spec, data, ctr = list())
spec |
Model specification created with |
data |
Vector (of size T) of observations. |
ctr |
A list of control parameters:
|
By default, OptimFUN
is set such that optimization is done via the well-known Broyden-
Fletcher-Goldfarb-Shanno (BFGS) algorithm using the optim
function with method =
"BFGS"
.
Starting values when par0
is not provided are chosen automatically
before optimization (see Ardia et al. (2019) for more details)
OptimFUN
allows for a custom optimizer to be used. The function must take
the form:
function(vPw, f_nll, spec, data, do.plm)
,
where vPw
are starting parameters (transformed), f_nll
is the function
to be minimize, spec
is the specification, data
is the data,
and do.plm
the originally inputed or default do.plm
.
The inputs spec
, data
, and do.plm
must be passed as inputs in the optimizer (see *Examples*).
It must output a list with the following elements:
value
: Optimal negative log-likelihood.
par
: Optimal parameters.
A list of class MSGARCH_ML_FIT
with the following elements:
par
: Vector (of size d) of optimal parameters.
loglik
: Log-likelihood of y
given the optimal parameters.
Inference
: list
with elements MatCoef
and Hessian
.
MatCoef
is a matrix (of size d x 4) with optimal parameter estimates, standard errors, t-stats, and p-values.
Hessian
is the Hessian (matrix of size d x d) of the negative log-likelihood function
evaluated at the optimal parameter estimates par
.
spec
: Model specification of class MSGARCH_SPEC
created with CreateSpec
.
data
: Vector (of size T) of observations.
ctr
: list
of the control used for the fit.
The MSGARCH_ML_FIT
with the following methods:
AIC
: Akaike Information Criterion (AIC).
BIC
: Bayesian Information Criterion (BIC).
simulate
: Simulation.
Volatility
: In-sample conditional volatility.
predict
: Forecast of the conditional volatility (and predictive distribution).
UncVol
: Unconditional volatility.
PredPdf
: Predictive density (pdf).
PIT
: Probability Integral Transform.
Risk
: Value-at-Risk and Expected-Shortfall.
State
: State probabilities (smoothed, filtered, predictive, Viterbi).
ExtractStateFit
: Single-regime model extractor.
summary
: Summary of the fit.
Ardia, D. Bluteau, K. Boudt, K. Catania, L. Trottier, D.-A. (2019). Markov-switching GARCH models in R: The MSGARCH package. Journal of Statistical Software, 91(4), 1-38. doi: 10.18637/jss.v091.i04
# create model specification spec <- CreateSpec() # load data data("SMI", package = "MSGARCH") # fit the model on the data by ML fit <- FitML(spec = spec, data = SMI) summary(fit) # custom optimizer example ## Not run: f_custom_optim <- function(vPw, f_nll, spec, data, do.plm){ out <- stats::optim(vPw, f_nll, spec = spec, data = data, do.plm = do.plm, method = "Nelder-Mead") return(out) } set.seed(123) fit <- FitML(spec, data = SMI, ctr = list(OptimFUN = f_custom_optim)) summary(fit) ## End(Not run)
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