R: Create Model Set for VARMA Models

search.varma {ldt}

R Documentation

Create Model Set for VARMA Models

Description

Use this function to create a Vector Autoregressive Moving Average model set and search for the best models (and other information) based on in-sample and out-of-sample evaluation metrics.

Usage

search.varma(
  data = get.data(),
  combinations = get.combinations(),
  metrics = get.search.metrics(),
  modelChecks = get.search.modelchecks(),
  items = get.search.items(),
  options = get.search.options(),
  maxParams = c(1, 0, 0, 0, 0, 0),
  seasonsCount = 0,
  maxHorizon = 1,
  simUsePreviousEstim = FALSE,
  olsStdMultiplier = 2,
  lbfgsOptions = get.options.lbfgs()
)

Arguments

`data`	A list that determines data and other required information for the search process. Use `get.data()` function to generate it from a `matrix` or a `data.frame`.
`combinations`	A list that determines the combinations of endogenous and exogenous variables in the search process. Use `get.combinations()` function to define it.
`metrics`	A list of options for measuring performance. Use get.search.metrics function to get them.
`modelChecks`	A list of options for excluding a subset of the model set. Use get.search.modelchecks function to get them.
`items`	A list of options for specifying the purpose of the search. Use get.search.items function to get them.
`options`	A list of extra options for performing the search. Use get.search.options function to get them.
`maxParams`	An integer vector that determines the maximum values for the parameters of the VARMA model: `(p,d,q,P,D,Q)`. If `NULL`, `c(2,0,0,0,0,0)` is used.
`seasonsCount`	An integer value representing the number of observations per unit of time.
`maxHorizon`	An integer value representing the maximum value for the prediction horizon if `type1` is `TRUE` in the `modelChecks` argument. Also, it is used as the maximum prediction horizon in checking predictions.
`simUsePreviousEstim`	If `TRUE`, parameters are initialized only in the first step of the simulation. The initial values of the n-th simulation (with one more observation) are the estimations from the previous step.
`olsStdMultiplier`	A number used as a multiplier for the standard deviation of OLS, used for restricting maximum likelihood estimation.
`lbfgsOptions`	A list containing L-BFGS optimization options. Use get.options.lbfgs function for initialization.

Value

A nested list with the following members:

`counts`	Information about the expected number of models, number of estimated models, failed estimations, and some details about the failures.
`results`	A data frame with requested information in `items` list.
`info`	The arguments and some general information about the search process such as the elapsed time.

Note that the output does not contain any estimation results, but minimum required data to estimate the models (Use summary() function to get the estimation).

Examples

# We simulate some data for this example:
set.seed(340)
n = 100
num_eq <- 3L
num_ar <- 2L
num_ma <- 1L
num_ma <- 1L
num_exo <- 2L
sample <- sim.varma(num_eq, arList = num_ar, maList = num_ma, exoCoef = num_exo, nObs = n)

# (relatively large) number of irrelevant explanatory variables:
num_y_ir <- 10
y_ir <- lapply(1:num_y_ir, function(x) rnorm(n))

# prepare data:
data <- data.frame(sample$y, y_ir, sample$x)
colnames(data) <- c(colnames(sample$y), paste0("w", 1:num_y_ir), colnames(sample$x))


# Create a VARMA model set:
y_sizes = 3 # assuming we know the number of relevant endogenous variables
metric_options <- get.search.metrics(typesIn = c("aic")) # We use SIC for searching
search_res <- search.varma(data = get.data(data, endogenous = num_eq + num_y_ir),
                           combinations = get.combinations(sizes = y_sizes,
                                                           numTargets = 3),
                           metrics = metric_options,
                           maxHorizon = 0)
print(search_res)