covid_volatility_correction: Lenza-Primiceri Covid Shock Correction

In tylerJPike/sovereign: State-Dependent Empirical Analysis

Lenza-Primiceri Covid Shock Correction

Description

Implement the deterministic volatility correction method of Lenza, Michele and Giorgio Primiceri "How to Estimate a VAR after March 2020" (2020) [NBER Working Paper]. Correction factors are estimated via maximum likelihood.

Usage

covid_volatility_correction(var, theta_initial = c(5, 2, 1.5, 0.8))

Arguments

var	VAR object
theta_initial	double: four element vector with scaling parameters, theta in Lenza and Primiceri (2020)

Value

var object

See Also

VAR()

var_irf()

var_fevd()

var_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('2018-01-01'), 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)

# correct VAR for COVID shock
var = sovereign::covid_volatility_correction(var)

# 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)

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