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x13_spec and x13_pickmdl wrapped as a single function

x13_both.Rd

Output is determined by the parameter both_output.

Usage

x13_both(
  ts,
  spec = NULL,
  ...,
  context = NULL,
  userdefined = NULL,
  both_output = "main",
  corona = FALSE,
  pickmdl_method = "first",
  star = 1,
  when_star = warning,
  when_automdl = message,
  when_finalnotok = NULL,
  identification_end = NULL,
  identification_estimate.to = NULL,
  policy = "Outliers",
  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
)

Arguments

ts

x13 parameter

spec

A "JD3_X13_SPEC" class object as generated with x13_spec or a list of several such objects as outputted from x13_spec_pickmdl. In the case of a single object and when automdl.enabled is FALSE, spec will be converted internally by x13_spec_pickmdl with default five arima model specifications.

...

Specification setting on the form function__parameter. See examples.

context

Parameter to x13. List of external regressors (calendar or other) to be used for estimation.

userdefined

Parameter to x13 (via ... to x13_pickmdl).

both_output

One of "main" (default, x13_pickmdl output), "spec" (spec output) or "both".

corona

Whether to update spec by outliers according to corona_outliers. FALSE or NULL means no update. TRUE or "ssb" means update.

pickmdl_method

crit_selection parameter or one of the two extra possibilities, "first_automdl" or "first_tryautomdl". In both cases the crit_selection parameter 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, the star parameter changes.

  • "first_tryautomdl": When no pickmdl model is ok: The automdl model is chosen if this model is ok, otherwise the star model is chosen.

star

crit_selection parameter

when_star

crit_selection parameter

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. See crit_ok.

identification_end

To shorten the series before runs used to identify (arima) parameters. That is, the series is shortened by window(ts, end = identification_end).

identification_estimate.to

To set set_estimate parameter d1 before runs used to identify (arima) parameters. This is an alternative to identification_end.

policy

Which refresh policy when model identification by shortened series. See x13_refresh.

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.mu is identified by the shortened series (see arima_mu).

automdl.enabled

Logical value or any other value.

  • When set to FALSE (the default), the pickmdl routine is applied.

  • When set to TRUE, the automdl routine is performed.

  • For any value other than TRUE or FALSE, the ARIMA model is chosen as specified by spec.

Note that when automdl.enabled is not FALSE, if spec is a list containing several objects outputted from x13_spec_pickmdl, only the first object is used.

fastfirst

When TRUE and when pickmdl with crit_selection parameter "first", only as many models as needed are run. This affects the output when output = "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 with ok, ok_final and mdl_nr) will be added to the x13 output object. Use comment to get the attribute or ok to get the attribute converted to a list.

Value

By default an x13 output object, or otherwise a list as specified by parameter output and both_output.

Details

All parameters except both_output and ... are parameters to x13_pickmdl.

Examples


myseries <- pickmdl_data("myseries")

a <- x13_both(myseries, spec = "rsa3", set_transform__fun = "Log", verbose = TRUE)
#> [1] "SARIMA model: (2,1,0) (0,1,1)"
summary(a)
#> Model: X-13
#> Log-transformation: yes 
#> SARIMA model: (2,1,0) (0,1,1)
#> 
#> Coefficients
#>           Estimate Std. Error  T-stat Pr(>|t|)    
#> phi(1)     0.98483    0.06035  16.318   <2e-16 ***
#> phi(2)     0.57655    0.06041   9.544   <2e-16 ***
#> btheta(1) -0.89213    0.07920 -11.264   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Regression model:
#>                 Estimate Std. Error T-stat Pr(>|t|)    
#> TC (2011-06-01) -0.19046    0.04705 -4.048 7.61e-05 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> Number of observations: 201, Number of effective observations: 188, Number of parameters: 5
#> Loglikelihood: 230.554, Adjusted loglikelihood: -624.9538
#> Standard error of the regression (ML estimate): 0.06727615 
#> AIC: 1259.908, AICc: 1260.237, BIC: 1276.09
#> 
#> Decomposition
#> Monitoring and Quality Assessment Statistics: 
#>     M stats
#> m1    0.576
#> m2    0.597
#> m3    3.000
#> m4    0.348
#> m5    3.000
#> m6    0.737
#> m7    0.223
#> m8    0.364
#> m9    0.166
#> m10   0.394
#> m11   0.368
#> q     0.963
#> qm2   1.008
#> 
#> Final filters: 
#> Seasonal filter: FILTER_S3X5
#> Trend filter: 23 terms Henderson moving average
#> 
#> Diagnostics
#> Residual seasonality tests
#> Error in print_diagnostics(x$diagnostics, digits = digits, ...): object 'residual_tests' not found

# is equivalent to

spec_a <- x13_spec("rsa3")
spec_a <- rjd3toolkit::set_transform(spec_a, fun = "Log")
a2 <- x13_both(myseries,spec_a,verbose=TRUE)
#> [1] "SARIMA model: (2,1,0) (0,1,1)"
a2
#> Serie span: All 
#> 
#> Model: X-13
#> Log-transformation: yes 
#> SARIMA model: (2,1,0) (0,1,1)
#> 
#> SARIMA coefficients:
#>    phi(1)    phi(2) btheta(1) 
#>    0.9848    0.5766   -0.8921 
#> 
#> Regression model:
#> TC (2011-06-01) 
#>         -0.1905 
#> 
#>  Seasonal filter: FILTER_S3X5;  Trend filter: H-23 terms
#>  M-Statistics: q Good (0.963); q-m2 Bad (1.008)
#>  QS test on SA: Good (1.000);  F-test on SA: Good (0.666)
#> 
#> For a more detailed output, use the 'summary()' function.

# several specification settings:

b <- x13_both(myseries, spec="rsa3",
  set_transform__fun = "None",
  set_easter__enable = TRUE,
  set_easter__duration = 3,
  set_easter__test = "None",
  set_outlier__outliers.type = c("LS","AO"),
  set_outlier__critical.value = 3
)
#> Warning: No model is ok according to criteria

# user defined regressors and modelling context