x13 with PICKMDL and partial concurrent possibilities
x13_pickmdl.Rdx13 can be run as usual (automdl) or with a PICKMDL specification.
The ARIMA model, outliers and filters can be identified at a certain date and then held fixed (with a new outlier-span).
Usage
x13_pickmdl(
series,
spec,
corona = FALSE,
...,
pickmdl_method = "first",
star = 1,
when_star = warning,
when_automdl = message,
when_finalnotok = NULL,
identification_end = NULL,
identification_estimate.to = NULL,
identify_t_filter = FALSE,
identify_s_filter = FALSE,
identify_outliers = TRUE,
identify_arima_mu = TRUE,
automdl.enabled = FALSE,
fastfirst = TRUE,
verbose = FALSE,
output = "sa",
add_comment = TRUE,
old_crit2 = NA
)
x13_automdl(..., automdl.enabled = TRUE)Arguments
- series
x13parameter- spec
An
x13_specoutput object or a list of several objects as outputted fromx13_spec_pickmdl. In the case of a single object and whenautomdl.enabledisFALSE,specwill be converted internally byx13_spec_pickmdlwith default five arima model specifications.- corona
Whether to update
specby outliers according tocorona_outliers.FALSEorNULLmeans no update.TRUEor"ssb"means update.- ...
Further
x13parameters (currently only parameteruserdefinedis additional parameter tox13).- pickmdl_method
crit_selectionparameter or one of the two extra possibilities,"first_automdl"or"first_tryautomdl". In both cases thecrit_selectionparameter is"first"and the automdl model is added as the last pickmdl model."first_automdl": The automdl model is chosen whenever no pickmdl model is ok. In other words, thestarparameter changes."first_tryautomdl": When no pickmdl model is ok: The automdl model is chosen if this model is ok, otherwise thestarmodel is chosen.
- star
crit_selectionparameter- when_star
crit_selectionparameter- when_automdl
Function to be called when automdl since no pickmdl model ok. Supply NULL to do nothing.
- when_finalnotok
Function to be called, e.g.
warning, when final run with final model is not ok. Supply NULL to do nothing. Seecrit_ok.- identification_end
To shorten the series before runs used to identify (arima) parameters. That is, the series is shortened by
window(series,end = identification_end).- identification_estimate.to
To set
x13_specparameterestimate.tobefore runs used to identify (arima) parameters. This is an alternative toidentification_end.- identify_t_filter
When
TRUE, Henderson trend filter is identified by the shortened (see above) series.- identify_s_filter
When
TRUE, Seasonal moving average filter is identified by the shortened series.- identify_outliers
When
TRUE, Outliers are identified by the shortened series.- identify_arima_mu
When
TRUE,arima.muis identified by the shortened series (seearima_mu).- automdl.enabled
When
TRUE, automdl is performed instead of pickmdl. Ifspecis a list of several objects as outputted fromx13_spec_pickmdl, only first object is used.- fastfirst
When
TRUEand when pickmdl withcrit_selectionparameter"first", only as many models as needed are run. This affects the output whenoutput = "all".- verbose
Printing information to console when
TRUE.- output
One of
"sa"(default),"spec"(final spec),"sa_spec"(both) and"all". See examples.- add_comment
When
TRUE, a comment attribute (character vector withok,ok_finalandmdl_nr) will be added to thex13output object. Usecommentto get the attribute orokto get the attribute converted to a list.- old_crit2
Logical. The p-value criterion used for PICKMDL criterion number 2. Set to
FALSEfor "Ljung-Box" and toTRUEfor "Ljung-Box (residuals at seasonal lags)". This parameter can be overridden by setting the"pickmdl.old_crit2"option, in which case the option value will take precedence. The default value (NA) means thatold_crit2 = (date_found < "2024-10-15"), wheredate_foundrefers to the end date according toidentification_endoridentification_estimate.to. If none of these is specified,old_crit2is set toFALSE. The default value is chosen to ensure that the new criterion is phased in automatically.
Examples
myseries <- pickmdl_data("myseries")
spec_a <- x13_spec(spec = "RSA3", transform.function = "Log")
a <- x13_pickmdl(myseries, spec_a, verbose = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> p d q bp bd bq
#> 2 1 0 0 1 1
#> outlier.from updated: 2021-10-01 New outliers: 2011-06
comment(a)
#> ok ok_final mdl_nr
#> "TRUE" "TRUE" "3"
ok(a)
#> $ok
#> [1] TRUE
#>
#> $ok_final
#> [1] TRUE
#>
#> $mdl_nr
#> [1] 3
#>
unlist(ok(a))
#> ok ok_final mdl_nr
#> 1 1 3
a$regarima
#> y = regression model + arima (2, 1, 0, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) 0.9848 0.061
#> Phi(2) 0.5766 0.061
#> BTheta(1) -0.8921 0.045
#>
#> Estimate Std. Error
#> TC (6-2011) -0.1905 0.047
#>
#>
#> Residual standard error: 0.06728 on 183 degrees of freedom
#> Log likelihood = 230.6, aic = 1260 aicc = 1260, bic(corrected for length) = -5.286
#>
a2 <- x13_pickmdl(myseries, spec_a, identification_end = c(2014, 2))
a2$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8434 0.040
#> BTheta(1) -0.9735 0.044
#>
#>
#> Residual standard error: 0.0707 on 185 degrees of freedom
#> Log likelihood = 216, aic = 1285 aicc = 1285, bic(corrected for length) = -5.243
#>
# As above, another way
a3 <- x13_pickmdl(myseries, spec_a, identification_estimate.to = "2014-03-01")
a3$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8434 0.040
#> BTheta(1) -0.9735 0.044
#>
#>
#> Residual standard error: 0.0707 on 185 degrees of freedom
#> Log likelihood = 216, aic = 1285 aicc = 1285, bic(corrected for length) = -5.243
#>
a4 <- x13_automdl(myseries, spec_a, identification_end = c(2014, 2))
a4$regarima
#> y = regression model + arima (1, 0, 2, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) -0.9522 0.030
#> Theta(1) -1.1092 0.072
#> Theta(2) 0.3625 0.071
#> BTheta(1) -1.0000 0.045
#>
#> Estimate Std. Error
#> Mean 0.02675 0.004
#>
#>
#> Residual standard error: 0.06489 on 183 degrees of freedom
#> Log likelihood = 232, aic = 1268 aicc = 1268, bic(corrected for length) = -5.331
#>
# As above, another way
spec_a_single <- x13_spec(spec = "RSA3", transform.function = "Log")
a5 <- x13_automdl(myseries, spec_a_single, identification_estimate.to = "2014-03-01")
a5$regarima
#> y = regression model + arima (1, 0, 2, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) -0.9522 0.030
#> Theta(1) -1.1092 0.072
#> Theta(2) 0.3625 0.071
#> BTheta(1) -1.0000 0.045
#>
#> Estimate Std. Error
#> Mean 0.02675 0.004
#>
#>
#> Residual standard error: 0.06489 on 183 degrees of freedom
#> Log likelihood = 232, aic = 1268 aicc = 1268, bic(corrected for length) = -5.331
#>
allvar <- pickmdl_data("allvar")
spec_b <- x13_spec(
spec = "RSA3", transform.function = "Log",
usrdef.varEnabled = TRUE,
usrdef.varType = c("Calendar", "Calendar"),
usrdef.var = allvar,
outlier.enabled = FALSE,
usrdef.outliersEnabled = TRUE,
usrdef.outliersType = rep("LS", 20),
usrdef.outliersDate = c("2009-01-01", "2016-01-01",
"2020-03-01", "2020-04-01", "2020-05-01",
"2020-06-01", "2020-07-01", "2020-08-01",
"2020-09-01", "2020-10-01", "2020-11-01",
"2020-12-01", "2021-01-01", "2021-02-01",
"2021-03-01", "2021-04-01", "2021-05-01",
"2021-06-01", "2021-07-01", "2021-08-01"))
b <- x13_pickmdl(myseries, spec_b, identification_end = c(2020, 2))
b$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8214 0.045
#> BTheta(1) -0.8562 0.051
#>
#> Estimate Std. Error
#> arb_dag 0.010938 0.001
#> skuddar 0.055092 0.028
#> LS (1-2009) -0.090294 0.030
#> LS (1-2016) -0.039840 0.030
#> LS (3-2020) -0.004550 0.053
#> LS (4-2020) -0.126323 0.069
#> LS (5-2020) 0.067283 0.068
#> LS (6-2020) -0.013231 0.068
#> LS (7-2020) 0.108865 0.068
#> LS (8-2020) -0.118575 0.069
#> LS (9-2020) 0.043492 0.068
#> LS (10-2020) -0.021723 0.068
#> LS (11-2020) 0.027382 0.068
#> LS (12-2020) 0.031743 0.069
#> LS (1-2021) -0.071634 0.069
#> LS (2-2021) -0.004503 0.069
#> LS (3-2021) 0.009891 0.069
#> LS (4-2021) 0.024852 0.069
#> LS (5-2021) -0.051741 0.069
#> LS (6-2021) -0.035384 0.070
#> LS (7-2021) -0.017531 0.069
#> LS (8-2021) 0.056976 0.060
#>
#>
#> Residual standard error: 0.04928 on 163 degrees of freedom
#> Log likelihood = 290.6, aic = 1180 aicc = 1188, bic(corrected for length) = -5.352
#>
# automdl instead
b1 <- x13_automdl(myseries, spec_b, identification_end = c(2020, 2))
b1$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8214 0.045
#> BTheta(1) -0.8562 0.051
#>
#> Estimate Std. Error
#> arb_dag 0.010938 0.001
#> skuddar 0.055092 0.028
#> LS (1-2009) -0.090294 0.030
#> LS (1-2016) -0.039840 0.030
#> LS (3-2020) -0.004550 0.053
#> LS (4-2020) -0.126323 0.069
#> LS (5-2020) 0.067283 0.068
#> LS (6-2020) -0.013231 0.068
#> LS (7-2020) 0.108865 0.068
#> LS (8-2020) -0.118575 0.069
#> LS (9-2020) 0.043492 0.068
#> LS (10-2020) -0.021723 0.068
#> LS (11-2020) 0.027382 0.068
#> LS (12-2020) 0.031743 0.069
#> LS (1-2021) -0.071634 0.069
#> LS (2-2021) -0.004503 0.069
#> LS (3-2021) 0.009891 0.069
#> LS (4-2021) 0.024852 0.069
#> LS (5-2021) -0.051741 0.069
#> LS (6-2021) -0.035384 0.070
#> LS (7-2021) -0.017531 0.069
#> LS (8-2021) 0.056976 0.060
#>
#>
#> Residual standard error: 0.04928 on 163 degrees of freedom
#> Log likelihood = 290.6, aic = 1180 aicc = 1188, bic(corrected for length) = -5.352
#>
# effect of identify_t_filter and identify_s_filter
set.seed(1)
rndseries <- ts(rep(1:12, 20) + (1 + (1:240)/20) * runif(240) + 0.5 * c(rep(1, 120), (1:120)^2),
frequency = 12, start = c(2000, 1))
spec_c <- x13_spec(outlier.enabled = FALSE)
c1 <- x13_automdl(rndseries, spec_c, identification_end = c(2009, 12))
c1$decomposition
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.200
#> M(2) 0.024
#> M(3) 0.020
#> M(4) 0.139
#> M(5) 0.268
#> M(6) 0.783
#> M(7) 1.307
#> M(8) 0.915
#> M(9) 0.730
#> M(10) 0.120
#> M(11) 0.120
#> Q 0.472
#> Q-M2 0.535
#>
#> Final filters:
#> Seasonal filter: 3x3
#> Trend filter: 13 terms Henderson moving average
c2 <- x13_automdl(rndseries, spec_c, identification_end = c(2009, 12), identify_t_filter = TRUE)
c2$decomposition
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.248
#> M(2) 0.023
#> M(3) 0.000
#> M(4) 0.139
#> M(5) 0.289
#> M(6) 0.768
#> M(7) 1.288
#> M(8) 0.933
#> M(9) 0.731
#> M(10) 0.060
#> M(11) 0.059
#> Q 0.470
#> Q-M2 0.533
#>
#> Final filters:
#> Seasonal filter: 3x3
#> Trend filter: 23-Henderson
c3 <- x13_automdl(rndseries, spec_c, identification_end = c(2009, 12), identify_t_filter = TRUE,
identify_s_filter = TRUE)
c3$decomposition
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.378
#> M(2) 0.032
#> M(3) 0.069
#> M(4) 0.338
#> M(5) 0.317
#> M(6) 0.751
#> M(7) 1.270
#> M(8) 0.830
#> M(9) 0.735
#> M(10) 0.093
#> M(11) 0.092
#> Q 0.531
#> Q-M2 0.592
#>
#> Final filters:
#> Seasonal filter: 3x5
#> Trend filter: 23-Henderson
# Warning when transform.function = "None"
spec_d <- x13_spec(spec = "RSA3", transform.function = "None")
d <- x13_pickmdl(myseries, spec_d, verbose = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> Warning: No model is ok according to criteria
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2021-10-01 No new outliers.
# Warning avoided (when_star) and 2nd (star) model selected
d2 <- x13_pickmdl(myseries, spec_d, star = 2, when_star = NULL, verbose = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> p d q bp bd bq
#> 0 1 2 0 1 1
#> outlier.from updated: 2021-10-01 No new outliers.
# automdl since no pickmdl model ok, but still not ok
d3 <- x13_pickmdl(myseries, spec_d, pickmdl_method = "first_automdl", verbose = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> Warning: No model is ok according to criteria
#> p d q bp bd bq
#> 2 1 1 0 1 1
#> automdl since no pickmdl model ok
#> outlier.from updated: 2021-10-01 No new outliers.
# airline model (star) since automdl also not ok
d4 <- x13_pickmdl(myseries, spec_d, pickmdl_method = "first_tryautomdl", verbose = TRUE,
when_finalnotok = warning) # also finalnotok warning
#> old_crit2 set to FALSE since no identification specification.
#> Warning: No model is ok according to criteria
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2021-10-01 No new outliers.
#> Warning: FINAL RUN NOT OK
# As a2, with output = "all"
k <- x13_pickmdl(myseries, spec_b, identification_end = c(2010, 2), output = "all",
fastfirst = FALSE) # With TRUE only one model in this case
k$sa$decomposition # As a2$decomposition
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.343
#> M(2) 0.349
#> M(3) 1.726
#> M(4) 0.435
#> M(5) 1.870
#> M(6) 0.702
#> M(7) 0.157
#> M(8) 0.197
#> M(9) 0.118
#> M(10) 0.160
#> M(11) 0.153
#> Q 0.610
#> Q-M2 0.646
#>
#> Final filters:
#> Seasonal filter: 3x9
#> Trend filter: 23-Henderson
k$mdl_nr # index of selected model used to identify parameters
#> [1] 1
k$sa_mult[[k$mdl_nr]]$decomposition # decomposition for model to identify
#> Monitoring and Quality Assessment Statistics:
#> M stats
#> M(1) 0.264
#> M(2) 0.248
#> M(3) 1.539
#> M(4) 0.000
#> M(5) 3.000
#> M(6) 1.056
#> M(7) 0.285
#> M(8) 0.000
#> M(9) 0.000
#> M(10) 0.000
#> M(11) 0.000
#> Q 0.721
#> Q-M2 0.816
#>
#> Final filters:
#> Seasonal filter: 3x5
#> Trend filter: 23-Henderson
k$crit_tab # Table of criteria
#> crit1 crit2 crit3 m_aic
#> [1,] 0.04327429 0.4368229 -0.7117072 344.0338
#> [2,] 0.04325725 0.5369880 -0.7497611 345.7862
#> [3,] 0.04875133 0.5635680 0.0000000 344.3432
#> [4,] 0.04160156 0.9047367 -0.9999670 345.5339
#> [5,] 0.04947505 0.6257011 -0.3132899 346.8342
# Effect of identify_outliers (TRUE is default)
m1 <- x13_pickmdl(myseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = FALSE)
m2 <- x13_pickmdl(myseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = TRUE,
verbose = TRUE, output = "all")
#> old_crit2 set to TRUE based on the date found (2010-03-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2010-03-01 New outliers: 2008-03
m3 <- x13_pickmdl(myseries, m2$spec, identification_end = c(2018, 2), identify_outliers = TRUE,
verbose = TRUE)
#> old_crit2 set to TRUE based on the date found (2018-03-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 2 1 2 0 1 1
#> outlier.from updated: 2018-03-01 New outliers: 2011-06, 2016-03
m1$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8434 0.040
#> BTheta(1) -0.9735 0.044
#>
#>
#> Residual standard error: 0.0707 on 185 degrees of freedom
#> Log likelihood = 216, aic = 1285 aicc = 1285, bic(corrected for length) = -5.243
#>
m2$sa$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.8271 0.042
#> BTheta(1) -0.9189 0.044
#>
#> Estimate Std. Error
#> AO (3-2008) -0.2046 0.068
#>
#>
#> Residual standard error: 0.07051 on 184 degrees of freedom
#> Log likelihood = 220.4, aic = 1278 aicc = 1279, bic(corrected for length) = -5.22
#>
m3$regarima
#> y = regression model + arima (2, 1, 2, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) 0.8897 0.145
#> Phi(2) 0.4636 0.080
#> Theta(1) -0.2235 0.152
#> Theta(2) -0.2411 0.132
#> BTheta(1) -0.8802 0.045
#>
#> Estimate Std. Error
#> AO (3-2008) -0.1418 0.054
#> TC (6-2011) -0.1651 0.040
#> AO (3-2016) -0.1853 0.054
#>
#>
#> Residual standard error: 0.06174 on 179 degrees of freedom
#> Log likelihood = 247.1, aic = 1235 aicc = 1236, bic(corrected for length) = -5.347
#>
# With corona outliers (even possible when series is not long enough)
m4 <- x13_pickmdl(myseries, spec_a, verbose = TRUE, corona = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> p d q bp bd bq
#> 2 1 0 0 1 1
#> outlier.from updated: 2021-10-01 New outliers: 2011-06
m4$regarima
#> y = regression model + arima (2, 1, 0, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) 1.0034 0.062
#> Phi(2) 0.5914 0.062
#> BTheta(1) -1.0000 0.050
#>
#> Estimate Std. Error
#> LS (3-2020) 0.07527 0.065
#> LS (4-2020) -0.19815 0.092
#> LS (5-2020) 0.05206 0.096
#> LS (6-2020) 0.02156 0.096
#> LS (7-2020) 0.14629 0.100
#> LS (8-2020) -0.22966 0.102
#> LS (9-2020) 0.15744 0.102
#> LS (10-2020) -0.07074 0.103
#> LS (11-2020) 0.02439 0.103
#> LS (12-2020) 0.09929 0.103
#> LS (1-2021) -0.19861 0.103
#> LS (2-2021) 0.06577 0.103
#> LS (3-2021) 0.06252 0.103
#> LS (4-2021) -0.01084 0.103
#> LS (5-2021) -0.11020 0.103
#> LS (6-2021) 0.04380 0.103
#> LS (7-2021) -0.04410 0.103
#> LS (8-2021) -0.03237 0.103
#> LS (9-2021) 0.18665 0.103
#> TC (6-2011) -0.19161 0.045
#>
#>
#> Residual standard error: 0.05883 on 164 degrees of freedom
#> Log likelihood = 248.2, aic = 1263 aicc = 1270, bic(corrected for length) = -5.025
#>
m5 <- x13_pickmdl(myseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = TRUE,
verbose = TRUE, corona = TRUE)
#> old_crit2 set to TRUE based on the date found (2010-03-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2010-03-01 New outliers: 2008-03
m5$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: no
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.7955 0.048
#> BTheta(1) -0.9997 0.050
#>
#> Estimate Std. Error
#> LS (3-2020) 2.0624 5.852
#> LS (4-2020) -19.8622 7.464
#> LS (5-2020) 9.9601 7.432
#> LS (6-2020) 2.2136 7.422
#> LS (7-2020) 3.4862 7.422
#> LS (8-2020) -11.0665 7.432
#> LS (9-2020) 18.5012 7.432
#> LS (10-2020) -8.2873 7.429
#> LS (11-2020) 2.7988 7.423
#> LS (12-2020) 5.2338 7.423
#> LS (1-2021) -17.0809 7.423
#> LS (2-2021) 3.4744 7.408
#> LS (3-2021) 4.6064 7.450
#> LS (4-2021) 0.8282 7.464
#> LS (5-2021) -11.0466 7.432
#> LS (6-2021) 8.4089 7.422
#> LS (7-2021) -13.3171 7.422
#> LS (8-2021) 4.1312 7.432
#> LS (9-2021) 24.9996 7.432
#> AO (3-2008) -18.9346 5.543
#> AO (3-2016) -19.5750 5.543
#> AO (8-2018) 19.7377 5.580
#> AO (4-2017) -15.8521 5.513
#> TC (10-2018) 14.0022 4.820
#> AO (3-2013) -19.3536 5.689
#> TC (1-2013) 13.9826 4.881
#>
#>
#> Residual standard error: 5.189 on 159 degrees of freedom
#> Log likelihood = -593.7, aic = 1245 aicc = 1256, bic(corrected for length) = 4.073
#>
########### quarterly series #############
qseries <- pickmdl_data("qseries")
# Effect of identify_outliers (TRUE is default)
q1 <- x13_pickmdl(qseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = FALSE)
q2 <- x13_pickmdl(qseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = TRUE,
verbose = TRUE, output = "all")
#> old_crit2 set to TRUE based on the date found (2010-07-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2010-07-01 New outliers: 2008-07
q3 <- x13_pickmdl(qseries, q2$spec, identification_end = c(2018, 2), identify_outliers = TRUE,
verbose = TRUE)
#> old_crit2 set to TRUE based on the date found (2018-07-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2018-07-01 New outliers: 2014-04
q1$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.2941 0.125
#> BTheta(1) -0.7877 0.104
#>
#>
#> Residual standard error: 0.04514 on 59 degrees of freedom
#> Log likelihood = 102.1, aic = 369.5 aicc = 369.9, bic(corrected for length) = -6.063
#>
q2$sa$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.4205 0.120
#> BTheta(1) -0.7619 0.106
#>
#> Estimate Std. Error
#> LS (III-2008) -0.134 0.037
#>
#>
#> Residual standard error: 0.04142 on 58 degrees of freedom
#> Log likelihood = 107.6, aic = 360.6 aicc = 361.3, bic(corrected for length) = -6.168
#>
q3$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.3971 0.122
#> BTheta(1) -0.7461 0.106
#>
#> Estimate Std. Error
#> LS (III-2008) -0.13357 0.035
#> AO (II-2014) 0.09058 0.031
#>
#>
#> Residual standard error: 0.03882 on 57 degrees of freedom
#> Log likelihood = 111.7, aic = 354.3 aicc = 355.4, bic(corrected for length) = -6.231
#>
# With corona outliers (even possible when series is not long enough)
q4 <- x13_pickmdl(qseries, spec_a, verbose = TRUE, corona = TRUE)
#> old_crit2 set to FALSE since no identification specification.
#> p d q bp bd bq
#> 2 1 0 0 1 1
#> outlier.from updated: 2021-10-01 New outliers: 2008-07
q4$regarima
#> y = regression model + arima (2, 1, 0, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Phi(1) 0.4350 0.134
#> Phi(2) 0.3402 0.130
#> BTheta(1) -1.0000 0.103
#>
#> Estimate Std. Error
#> LS (I-2020) -0.0398499 0.038
#> LS (II-2020) -0.0287782 0.042
#> LS (III-2020) -0.0745579 0.042
#> LS (IV-2020) 0.0850556 0.043
#> LS (I-2021) -0.0006202 0.043
#> LS (II-2021) -0.1151605 0.043
#> LS (III-2021) 0.0442247 0.043
#> LS (III-2008) -0.1253552 0.033
#>
#>
#> Residual standard error: 0.0335 on 50 degrees of freedom
#> Log likelihood = 116.8, aic = 358.1 aicc = 364.5, bic(corrected for length) = -6.06
#>
q5 <- x13_pickmdl(qseries, x13_spec("RSA3", outlier.usedefcv = FALSE, outlier.cv = 3),
identification_end = c(2010, 2), identify_outliers = TRUE,
verbose = TRUE, corona = TRUE)
#> old_crit2 set to TRUE based on the date found (2010-07-01) and threshold (2024-10-15).
#> p d q bp bd bq
#> 0 1 1 0 1 1
#> outlier.from updated: 2010-07-01 New outliers: 2008-07
q5$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.3110 0.136
#> BTheta(1) -0.8427 0.084
#>
#> Estimate Std. Error
#> LS (I-2020) -0.030835 0.035
#> LS (II-2020) -0.021382 0.036
#> LS (III-2020) -0.082770 0.036
#> LS (IV-2020) 0.077653 0.036
#> LS (I-2021) 0.002271 0.037
#> LS (II-2021) -0.098601 0.037
#> LS (III-2021) 0.034777 0.037
#> LS (III-2008) -0.133177 0.032
#> AO (II-2014) 0.093274 0.027
#> AO (II-2011) 0.085193 0.027
#>
#>
#> Residual standard error: 0.03105 on 49 degrees of freedom
#> Log likelihood = 124.8, aic = 344.2 aicc = 351.8, bic(corrected for length) = -6.146
#>
# Demonstrate strange behavior of x13 with TC at end. Updating outlier.from matters.
# Explains why TC (III-2021) is in q4 and not in q5
q11 <- x13(window(qseries, end = c(2020, 1)),
spec = x13_spec(spec = "RSA3", transform.function = "Log",
usrdef.outliersEnabled = TRUE, usrdef.outliersType = "TC",
usrdef.outliersDate = "2020-01-01"))
q12 <- x13(window(qseries, end = c(2020, 1)), # same with outlier.from
spec = x13_spec(spec = "RSA3", transform.function = "Log",
usrdef.outliersEnabled = TRUE, usrdef.outliersType = "TC",
usrdef.outliersDate = "2020-01-01", outlier.from = "2020-04-01"))
q11$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.2676 0.135
#> BTheta(1) -0.9100 0.093
#>
#>
#> Residual standard error: 0.04206 on 53 degrees of freedom
#> Log likelihood = 94.55, aic = 327.9 aicc = 328.4, bic(corrected for length) = -6.194
#>
q12$regarima
#> y = regression model + arima (0, 1, 1, 0, 1, 1)
#> Log-transformation: yes
#> Coefficients:
#> Estimate Std. Error
#> Theta(1) -0.2693 0.136
#> BTheta(1) -0.9248 0.092
#>
#> Estimate Std. Error
#> TC (I-2020) -0.03069 0.043
#>
#>
#> Residual standard error: 0.04166 on 52 degrees of freedom
#> Log likelihood = 94.81, aic = 329.4 aicc = 330.2, bic(corrected for length) = -6.141
#>