var_irf: Estimate impulse response functions

In sovereign: State-Dependent Empirical Analysis

Description

Estimate impulse response functions

Usage

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

Arguments

var	VAR 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

data.frame with columns target, shock, horizon, response.lower, response, response.upper

See Also

VAR()

var_irf()

var_fevd()

var_hd()

RVAR()

rvar_irf()

rvar_fevd()

rvar_hd()

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)

 # estimate VAR
  var =
    sovereign::VAR(
      data = Data,
      horizon = 10,
      freq = 'month',
      lag.ic = 'BIC',
      lag.max = 4)

 # impulse response functions
 var.irf = sovereign::var_irf(var)

 # forecast error variance decomposition
 var.fevd = sovereign::var_fevd(var)

 # historical shock decomposition
 var.hd = sovereign::var_hd(var)

sovereign documentation built on Jan. 5, 2022, 1:08 a.m.