case.test: test case

In GVAR: Vector Error Correction Model (VECM), VECM with exogenous I(1) variables, Global VAR (GVAR)

Usage

case.test(z.ts, etw, p, q = NULL, n = NULL, r, type = "vecm")

Arguments

z.ts
etw
p
q
n
r
type

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function(z.ts,etw,p,q=NULL,n=NULL,r,type="vecm")
   #  z.ts ... time series
   #   etw ... estimation time window as specified in estim.we.mdls.r
   #     p ... lag order
   #     n ... number of endogenous variables
   #     r ... cointegrating relations
   #  type ... model, either "vecm" or "we.vecm"
{
  if (is.null(n) && type=="we.vecm") stop("\'n\' missing",call.=FALSE)
  
  mdls <- list()
  test.stat <- vector()
  cv <- vector()
  p.value <- vector()
  
  T <- dim(z.ts)[1]-p
  m <- dim(z.ts)[2]
  cases <- c("I","II","III","IV","V")
  
  if (type=="we.vecm")
  {
  
     #     Johansen      ~      Pesaran
   
     # case<- "H_2(r)"   ~  case "I"    # \mu_t = 0
     # case<- "H_1^*(r)" ~  case "II"   # \mu_t = \alpha \rho_0
     # case<- "H_1(r)"   ~  case "III"  # \mu_t = \mu_0
     # case<- "H^*(r)"   ~  case "IV"   # \mu_t = \mu_0+\alpha \rho_1 t # \alpha_{\bot}'\mu_1=0
     # case<- "H(r)"     ~  case "V"    # \mu_t = \mu_0+\mu_1 t
      
    for (i in 1:5){mdls[[i]] <- est.we.mdls(z.ts=z.ts,etw=etw,p=p,q=q,r=r,n=n,case=cases[i])}

  } else {
    for (i in 1:5){mdls[[i]] <- est.vecm.mdls(Y.ts=z.ts,etw=etw,p=p,r=r,case=cases[i])}
  }
  
    # test statistics as in Johansen (1995) p. 161 f.
  
  for (i in c(1,3))
  {
    test.stat[i] <- T*sum(log(1-mdls[[5-i+1]]$lambda[(r+1):m])-log(1-mdls[[4-i+1]]$lambda[(r+1):m]))
    cv[i] <- qchisq(0.95,df=(m-r))
    p.value[i] <- 1-pchisq(test.stat[i],df=(m-r))
  }
  for (i in c(2,4))
  {
    test.stat[i] <- T*sum(log(1-mdls[[3-i+2]]$lambda[1:r])-log(1-mdls[[4-i+2]]$lambda[1:r]))
    cv[i] <- qchisq(0.95,df=r)
    p.value[i] <- 1-pchisq(test.stat[i],df=r)
  }

    # prepare & print output
      
  res <- cbind(round(test.stat,4),round(cv,4),round(p.value,4))
  rownames(res) <- c("c.1 restricted vs. c.1 unrestricted","       c.1 = 0 vs. c.1 restricted",
                     "c.0 restricted vs. c.0 unrestricted","       c.0 = 0 vs. c.0 restricted")
  colnames(res) <- c("     Statistic","   Crit.value","    p-value")
  
  cat("\nCase selection results:\n")
  print(res)
  }

GVAR documentation built on May 2, 2019, 6:30 p.m.