Seasonal Adjustment with TRAMO-SEATS
Usage
tramoseats(
ts,
spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
context = NULL,
userdefined = NULL
)
tramoseats_fast(
ts,
spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
context = NULL,
userdefined = NULL
)
.jtramoseats(
ts,
spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
context = NULL,
userdefined = NULL
)
Arguments
- ts
a univariate time series.
- spec
the model specification. Can be either the name of a predefined specification or a user-defined specification.
- context
the dictionnary of variables.
- userdefined
a vector containing the additional output variables (see
tramoseats_dictionary()
).
Value
The tramoseats()
function returns a list with the results, the
estimation specification and the result specification, while
tramoseats_fast()
is a faster function that only returns the results.
The .jtramoseats()
functions only results the java object to custom outputs
in other packages (use rjd3toolkit::dictionary()
to get the list of
variables and rjd3toolkit::result()
to get a specific variable).
Examples
library("rjd3toolkit")
sp <- tramoseats_spec("rsafull")
y <- rjd3toolkit::ABS$X0.2.09.10.M
tramoseats_fast(y, spec = sp)
#> Model: TRAMO-SEATS
#> Log-transformation: yes
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> -0.8278 -0.4255
#>
#> Regression model:
#> monday tuesday wednesday thursday friday
#> -0.0109446 0.0048940 0.0001761 0.0132928 -0.0024801
#> saturday lp easter AO (2000-06-01) AO (2000-07-01)
#> 0.0153509 0.0410667 0.0503888 0.1681662 -0.1972348
#>
#> For a more detailed output, use the 'summary()' function.
sp <- add_outlier(sp,
type = c("AO"), c("2015-01-01", "2010-01-01")
)
sp <- set_transform(
set_tradingdays(
set_easter(sp, enabled = FALSE),
option = "workingdays"
),
fun = "None"
)
tramoseats_fast(y, spec = sp)
#> Model: TRAMO-SEATS
#> Log-transformation: no
#> SARIMA model: (0,1,1) (0,1,1)
#>
#> SARIMA coefficients:
#> theta(1) btheta(1)
#> -0.8235 -0.2608
#>
#> Regression model:
#> monday tuesday wednesday thursday friday
#> -11.7873 0.2507 3.0039 12.8309 -5.4519
#> saturday lp AO (2010-01-01) AO (2015-01-01) AO (2000-06-01)
#> 17.2998 33.6083 40.5331 -10.7096 192.7411
#> AO (2000-07-01) AO (2005-04-01)
#> -200.7316 -177.1356
#>
#> For a more detailed output, use the 'summary()' function.