pvars (version 1.1.1)

PP: Persistence Profiles

Description

Calculates persistence profiles for each of the \(r\) long-run relationships.

Usage

PP.system(x, n.ahead = 20)
PP.variable(x, n.ahead = 20, shock = NULL)

Value

A list of class 'svarirf' holding the persistence profiles as a 'data.frame'.

Arguments

x

Rank-restricted VAR object of class 'varx' or any other that can be coerced to 'varx', e.g. 'vec2var'. If the object is also of child class 'id', PP.variable calculates the persistence profiles which are initiated by the provided structural shocks.

n.ahead

Integer. Number of periods to consider after the initial impulse, i.e. the horizon of the PP.

shock

Matrix. Each column vector specifies a set of simultaneous shocks, which initiate \(r\) persistence profiles. If NULL (the default), a separate unit impulse is set for each shock.

Functions

• PP.system(): PP due to a system-wide shock

• PP.variable(): PP due to a structural or variable-specific shock

References

Lee, K., C., Pesaran, M. H. (1993): "Persistence Profiles and Business Cycle Fluctuations in a Disaggregated Model of UK Output Growth", Richerche Economiche, 47, pp. 293-322.

Pesaran, M. H., and Shin, Y. (1996): "Cointegration and Speed of Convergence to Equilibrium", Journal of Econometrics, 71, pp. 117-143.

Examples

data("PCAP")
names_k = c("g", "k", "l", "y") # variable names
names_i = levels(PCAP$id_i)     # country names
L.data  = sapply(names_i, FUN=function(i) 
  ts(PCAP[PCAP$id_i==i, names_k], start=1960, end=2019, frequency=1), 
  simplify=FALSE)
  
# estimate VAR for DNK under rank-restriction r=2 #
dim_r  = 2  # cointegrataion rank
R.t_D1 = list(t_break=c(23, 49))  # trend breaks
R.vecm = VECM(y=L.data$DNK, dim_p=2, dim_r=dim_r, type="Case4", t_D1=R.t_D1)

# define shocks #
shock1 = diag(4)   # 4 separate shocks
shock2 = cbind(c(1, 0,  0, 0),  # positive shock on "g"
               c(0, 0, -1, 0),  # negative shock on "l"
               c(0, 0,  1, 1))  # simultaneous shocks

# calculate persistence profiles #
R.ppv1 = PP.variable(R.vecm, n.ahead=50, shock=shock1)
R.ppv2 = PP.variable(R.vecm, n.ahead=50, shock=shock2)
R.ppsy = PP.system(R.vecm, n.ahead=50)

# edit plots #
library("ggplot2")
as.pplot(ppv1=plot(R.ppv1), n.rows=4)$F.plot + guides(color="none")
as.pplot(ppv2=plot(R.ppv2), n.rows=3, color_g="black") # reshape facet array
plot(R.ppsy, selection=list(1, c(1,4)))  # dismiss cross-term PP