test_piar | R Documentation |
Test if a time series is periodically integrated.
test_piar(x, d, p, sintercept = FALSE, sslope = FALSE, homoschedastic = FALSE)
x |
time series. |
d |
period. |
p |
autoregressive order, a positive integer. |
sintercept |
if TRUE, include seasonal intercept. |
sslope |
if TRUE, include seasonal slope. |
homoschedastic |
if TRUE, assume the innovations variance is the same for all seasons. |
Computes test statistics for Franses (1996) test for periodic
integration of order 1. The test is based on periodic autoregression
of order p
, where p
can be any positive integer.
a list with the following components:
p |
autoregressive order. |
spec |
values of |
statistics |
a matrix containing the test statistics (first row) and the
corresponding p-values (second row). |
Currently only the case p = 1
is handled, for p > 1
the
statistics are set to NA. :TODO: handle this.
All statistics are computed but some p-values are not computed yet.
Georgi N. Boshnakov
Boswijk HP and Franses PH (1996). “Unit roots in periodic autoregressions.” Journal of Time Series Analysis, 17(3), pp. 221–245.
pclspiar
,
pclsdf
ts1 <- window(dataFranses1996[ , "CanadaUnemployment"],
start = c(1960, 1), end = c(1987, 4))
test_piar(ts1, 4, 1, sintercept = TRUE)
pcTest(ts1, "piar", 4, 1, sintercept = TRUE) # same
test_piar(ts1, 4, 1, sintercept = TRUE, sslope = TRUE)
test_piar(ts1, 4, 1)
test_piar(ts1, 4, 1, homoschedastic = TRUE)
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