rvar_irf: Estimate regime-dependent impulse response functions

In tylerJPike/sovereign: State-Dependent Empirical Analysis

Estimate regime-dependent impulse response functions

Description

Estimate regime-dependent impulse response functions

Usage

rvar_irf(
  rvar,
  horizon = 10,
  CI = c(0.1, 0.9),
  bootstrap.type = "auto",
  bootstrap.num = 100,
  bootstrap.parallel = FALSE,
  bootstrap.cores = -1
)

Arguments

rvar	RVAR output
horizon	int: number of periods
CI	numeric vector: c(lower ci bound, upper ci bound)
bootstrap.type	string: bootstrapping technique to use ('auto', 'standard', or 'wild'); if auto then wild is used for IV or IV-short, else standard is used
bootstrap.num	int: number of bootstraps
bootstrap.parallel	boolean: create IRF draws in parallel
bootstrap.cores	int: number of cores to use in parallel processing; -1 detects and uses half the available cores

Value

list of regimes, each with data.frame of columns target, shock, horizon, response.lower, response, response.upper

See Also

VAR()

var_irf()

var_fevd()

RVAR()

rvar_irf()

rvar_fevd()

Examples

 # simple time series
 AA = c(1:100) + rnorm(100)
 BB = c(1:100) + rnorm(100)
 CC = AA + BB + rnorm(100)
 date = seq.Date(from = as.Date('2000-01-01'), by = 'month', length.out = 100)
 Data = data.frame(date = date, AA, BB, CC)
 Data = dplyr::mutate(Data, reg = dplyr::if_else(AA > median(AA), 1, 0))

 # estimate VAR
  rvar =
    sovereign::RVAR(
      data = Data,
      horizon = 10,
      freq = 'month',
      regime.method = 'rf',
      regime.n = 2,
      lag.ic = 'BIC',
      lag.max = 4)

 # impulse response functions
 rvar.irf = sovereign::rvar_irf(rvar)

 # forecast error variance decomposition
 rvar.fevd = sovereign::rvar_fevd(rvar)

 # historical shock decomposition
 rvar.hd = sovereign::rvar_hd(rvar)

tylerJPike/sovereign documentation built on Nov. 15, 2022, 10:29 a.m.