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Seasonal Adjustment with TRAMO-SEATS

Source: R/tramoseats.R
tramoseats.Rd

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)
#> TRAMO
#> Log-transformation: yes 
#> SARIMA model:  (0,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat
#> theta(1)  -0.82783    0.02571 -32.196
#> btheta(1) -0.42554    0.06388  -6.661
#> 
#> Regression model:
#>                   Estimate Std. Error T-stat
#> monday          -0.0109446  0.0034805 -3.145
#> tuesday          0.0048940  0.0035307  1.386
#> wednesday        0.0001761  0.0034970  0.050
#> thursday         0.0132928  0.0035330  3.763
#> friday          -0.0024801  0.0035383 -0.701
#> saturday         0.0153509  0.0035171  4.365
#> lp               0.0410667  0.0101178  4.059
#> easter           0.0503888  0.0072698  6.931
#> AO (2000-06-01)  0.1681662  0.0299743  5.610
#> AO (2000-07-01) -0.1972348  0.0298664 -6.604
#> Number of observations:  425 
#> Number of effective observations:  412 
#> Number of parameters:  13 
#> 
#> Loglikelihood:  781.358 
#> Adjusted loglikelihood:  -2086.269 
#> 
#> Standard error of the regression (ML estimate):  0.03615788 
#> AIC:  4198.538 
#> AICC:  4199.452 
#> BIC:  4250.811 
#> 
#> 
#> Decomposition
#> model 
#> 
#> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1 
#> MA:  1 -0.8278316 0 0 0 0 0 0 0 0 0 0 -0.4255409 0.3522762 
#> var:  1 
#> 
#> trend 
#> 
#> DIF:  1 -2 1 
#> MA:  1 0.06437327 -0.9356267 
#> var:  0.004098184 
#> 
#> seasonal 
#> 
#> DIF:  1 1 1 1 1 1 1 1 1 1 1 1 
#> MA:  1 0.6197472 0.3121522 0.07325877 -0.1026014 -0.2222985 -0.2934508 -0.3240296 -0.3220203 -0.295141 -0.2506158 -0.1950007 
#> var:  0.1460322 
#> 
#> irregular 
#> 
#> var:  0.384587 
#> 
#> 
#> Diagnostics
#> Relative contribution of the components to the stationary
#> portion of the variance in the original series,
#> after the removal of the long term trend (in %)
#> 
#>            Component
#>  cycle         0.250
#>  seasonal     97.046
#>  irregular     0.600
#>  calendar      0.741
#>  others        0.325
#>  total        98.962
#> 
#> Residual seasonality tests
#>                 P.value
#>  seas.ftest.i         1
#>  seas.ftest.sa        1
#>  seas.qstest.i        1
#>  seas.qstest.sa       1
#>  td.ftest.i           1
#>  td.ftest.sa          1
#> 
#> 
#> Final
#> Last values
#>          series       sa    trend      seas       irr
#> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979
#> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681
#> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508
#> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588
#> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587
#> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303
#> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913
#> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187
#> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924
#> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726
#> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781
#> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960
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)
#> TRAMO
#> Log-transformation: no 
#> SARIMA model:  (0,1,1) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error T-stat
#> theta(1)  -0.82355    0.02896 -28.43
#> btheta(1) -0.26081    0.04903  -5.32
#> 
#> Regression model:
#>                  Estimate Std. Error T-stat
#> monday           -11.7873     3.8750 -3.042
#> tuesday            0.2507     3.8786  0.065
#> wednesday          3.0039     3.8746  0.775
#> thursday          12.8309     3.9423  3.255
#> friday            -5.4519     3.9132 -1.393
#> saturday          17.2998     3.9274  4.405
#> lp                33.6083    11.2479  2.988
#> AO (2010-01-01)   40.5331    33.7485  1.201
#> AO (2015-01-01)  -10.7096    33.7815 -0.317
#> AO (2000-06-01)  192.7411    33.9640  5.675
#> AO (2000-07-01) -200.7316    33.7986 -5.939
#> AO (2005-04-01) -177.1356    33.7257 -5.252
#> Number of observations:  425 
#> Number of effective observations:  412 
#> Number of parameters:  15 
#> 
#> Loglikelihood:  -2139.74 
#> Standard error of the regression (ML estimate):  43.47307 
#> AIC:  4309.481 
#> AICC:  4310.693 
#> BIC:  4369.796 
#> 
#> 
#> Decomposition
#> model 
#> 
#> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1 
#> MA:  1 -0.8235461 0 0 0 0 0 0 0 0 0 0 -0.2608057 0.2147855 
#> var:  1 
#> 
#> trend 
#> 
#> DIF:  1 -2 1 
#> MA:  1 0.09197092 -0.9080291 
#> var:  0.003491855 
#> 
#> seasonal 
#> 
#> DIF:  1 1 1 1 1 1 1 1 1 1 1 1 
#> MA:  1 0.6220678 0.315157 0.07593327 -0.1008043 -0.2215881 -0.2938052 -0.3252816 -0.3239249 -0.2974269 -0.2530226 -0.1973046 
#> var:  0.240472 
#> 
#> irregular 
#> 
#> var:  0.2654024 
#> 
#> 
#> Diagnostics
#> Relative contribution of the components to the stationary
#> portion of the variance in the original series,
#> after the removal of the long term trend (in %)
#> 
#>            Component
#>  cycle         0.198
#>  seasonal     98.409
#>  irregular     0.223
#>  calendar      0.246
#>  others        0.257
#>  total        99.333
#> 
#> Residual seasonality tests
#>                 P.value
#>  seas.ftest.i         1
#>  seas.ftest.sa        1
#>  seas.qstest.i        1
#>  seas.qstest.sa       1
#>  td.ftest.i           1
#>  td.ftest.sa          1
#> 
#> 
#> Final
#> Last values
#>          series       sa    trend        seas          irr
#> Sep 2016 1393.5 1557.498 1559.704 -163.997649  -2.20658744
#> Oct 2016 1497.4 1553.718 1557.296  -56.318379  -3.57749836
#> Nov 2016 1684.3 1538.837 1555.164  145.462939 -16.32645755
#> Dec 2016 2850.4 1544.533 1553.556 1305.866600  -9.02221292
#> Jan 2017 1428.5 1540.203 1552.499 -111.703036 -12.29569819
#> Feb 2017 1092.4 1547.827 1551.890 -455.426856  -4.06311664
#> Mar 2017 1370.3 1535.240 1551.720 -164.939565 -16.48081320
#> Apr 2017 1522.6 1581.396 1551.632  -58.795692  29.76367672
#> May 2017 1452.4 1552.527 1551.001 -100.127238   1.52621793
#> Jun 2017 1557.2 1549.996 1549.968    7.204385   0.02775996
#> Jul 2017 1445.5 1543.239 1548.979  -97.739206  -5.73936138
#> Aug 2017 1303.1 1544.526 1548.167 -241.425886  -3.64143843