dynamac (version 0.1.4)

pssbounds: Perform Pesaran, Shin and Smith (2001) cointegration test

Description

Perform Pesaran, Shin and Smith (2001) cointegration test

Usage

pssbounds(data = list(), obs = NULL, fstat = NULL, tstat = NULL,
  case = NULL, k = NULL, digits = 3, object.out = FALSE)

Arguments

data

an optional dynardl model. We highly recommend this option. Users are of course welcome to determine their own Case, t-statistic, F-statistic, and observations, but it is easier to have the model determine these quantities.

obs

number of observations

fstat

F-statistic of the joint test that variables in levels (except the lagged dependent variable) are equal to zero: l.y = l.x1 + l.x2 +...+l.xk = 0

tstat

t-statistic of the lagged dependent variable

case

specify certain restrictions on the constant and trend terms, since critical values differ by case. Case I: no intercept or trend, Case II: restricted intercept, no trend, Case III: unrestricted intercept with no trend, Case IV: unrestricted intercept and restricted trend, Case V: unrestricted intercept and trend. Case III is most frequently specified

k

number of regressors appearing in levels in the estimated model

digits

the number of digits to round to when showing output. We recommend three.

object.out

if TRUE, and dynardl.auto.correlated is assigned to an object, the AIC, BIC, and results will be stored for the user's convenience.

Details

pssbounds performs post-estimation cointegration testing using the bounds testing procedure from Pesaran, Shin, and Smith (2001). Since test statistics vary based on the number of k regressors, length of the series, these are required, in addition to F- and t-statistics.

Examples

# NOT RUN {
# Using the ineq data from dynamac
# We can get all the values by hand
ardl.model <- dynardl(concern ~ incshare10 + urate, data = ineq, 
        lags = list("concern" = 1, "incshare10" = 1),
        diffs = c("incshare10", "urate"), 
        lagdiffs = list("concern" = 1),
       ec = TRUE, simulate = FALSE)
summary(ardl.model)
pssbounds(obs = 47, fstat = 7.01578, tstat = -3.223, case = 3, k = 1)

# Or just pass a dynardl model.
pssbounds(ardl.model)
# }