Extract Components from kardl Objects

kardl_extract() is a generic accessor for retrieving selected documented components from objects produced by the kardl package.

Usage

kardl_extract(kardl_object, what, variable = NULL, component = NULL)

Arguments

kardl_object: A supported object produced by the kardl package.
what: A character string specifying the component to extract. The available options depend on the class of object and are documented in the sections below.
variable: An optional character string specifying a particular variable to extract results for, when applicable. The available options depend on the selected what value and are documented in the relevant sections below. This argument is available just for kardl_symmetric objects, where users can extract results for specific variables included in the symmetry test. For example, if a symmetry test includes variables "drivers" and "PetrolPrice", users can specify variable = "drivers" to extract results related to the "drivers" variable, or variable = c("drivers", "PetrolPrice") to extract results for both variables. If variable is not specified when extracting components that include variable-specific results, the function will return results for all variables included in the symmetry test.
component: An optional character string specifying a particular component to extract, when applicable. This argument is available just for kardl_symmetric objects, where users can specify whether they want to extract long-run or short-run results from the symmetry test. For example, if what is set to "long_wald_tests", users can specify component = "longrun" to extract long-run Wald test results, or component = "H0" to extract the long-run null hypothesis description. If component is not specified when extracting components that include multiple subcomponents (e.g., both long-run and short-run results), the function will return all available subcomponents for the selected what value.

Value

The requested component. The returned object depends on the class of object and the selected value of what.

Details

It provides a stable user-facing interface for accessing important results without requiring users to rely on the internal list structure of returned objects.

Supported classes

kardl_extract() currently supports:

kardl_lm
kardl_mplier
kardl_boot
kardl_test
kardl_test_summary
kardl_symmetric

Components for `kardl_lm` objects

For fitted kardl_lm model objects returned by kardl(), what may be one of:

data_ts_info: time-series information about the input data.
data_is_ts: whether the input data is a time series.
data_class: class of the input data.
data_start: starting time of the input data.
data_end: ending time of the input data.
data_frequency: frequency of the input data.
data_deltat: delta time of the input data.
data_tsp: time-series properties of the input data.
data_time: time index of the input data.
no_constant: Whether the model excludes an intercept.
trend: Trend specification.
asym_long_vars: Variables with long-run asymmetry.
asym_short_vars: Variables with short-run asymmetry.
deterministic: Deterministic regressors.
dependent_var: Dependent variable.
independent_vars: Independent variables.
all_vars: All variables used in the model.
all_asym_vars: All asymmetric variables.
indep_as_excluded: Short-run asymmetric variables excluded from the linear set.
indep_al_excluded: Long-run asymmetric variables excluded from the linear set.
short_run_vars: Short-run variables.
long_run_vars: Long-run variables.
shortrun_length: Number of short-run terms.
lag_rows_number: Number of rows lost due to lag construction.
model_type: Model type.
data: Prepared data used internally.
start_time: Starting time of the estimation sample.
end_time: Ending time of the estimation sample.
span: Time span of the estimation sample.
opt_lag: Selected optimal lag order.
lag_criteria: Lag-selection criterion values.
all_cr_lags: Candidate lag combinations and criterion values.
k: Number of regressors used in relevant post-estimation procedures.
n: Effective sample size.

Components for `kardl_mplier` objects

For dynamic multiplier objects returned by mplier(), what may be one of:

multipliers: Estimated dynamic multipliers.
omega: Adjustment-related multiplier matrix.
lambda: Short-run coefficient matrix used in multiplier construction.
vars: Variables included in the multiplier calculation.
horizon: Multiplier horizon.

Components for `kardl_boot` objects

For bootstrapped multiplier objects returned by bootstrap(), what may be one of:

multipliers: Bootstrapped dynamic multiplier results.
level: Confidence level used for bootstrap intervals.
replications: Number of bootstrap replications.
vars: Variables included in the bootstrap procedure.
horizon: Multiplier horizon.

Components for `kardl_test` objects

the kardl_test class includes objects returned by pssf(), psst(), and narayan()

For test objects, what may be one of:

type: Type of test.
case: Deterministic case used in the test.
statistic: Test statistic.
method: Test method description.
alternative: Alternative hypothesis.
data.name: Name of the data or model object.
sample.size: Effective sample size.
hypotheses: Textual description of the null and alternative hypotheses.
var_names: Variables involved in the test.
k: Number of regressors entering the bounds test.
n: Sample size, when available.
sig: Significance-level information.
notes: Additional notes.

Components for `kardl_test_summary` objects

the kardl_test_summary class includes summary objects produced by summary() methods for kardl_test objects, such as those produced by summary.pssf(), summary.psst(), and summary.narayan().

For test summary objects, what may be one of:

statistic: Test statistic.
case: Textual description of the deterministic case.
variables: Variables included in the tested restriction.
decision: Textual test decision.
hypotheses: Textual description of the null and alternative hypotheses.
numeric_decision: Numeric encoding of the test decision.
significance_level: Significance level used for the decision.
critical_values: Lower and upper critical-value bounds.
k: Number of regressors entering the bounds test.
notes: Additional notes.

Components for `kardl_symmetric` objects

The kardl_symmetric class includes objects returned by the symmetrytest() function, which tests for long-run and short-run asymmetry in models fitted with kardl() that include asymmetric terms. For symmetry test objects, what may be one of:

long_wald_summary: Summary of long-run Wald test results.
long_hypotheses: Textual description of long-run null and alternative hypotheses.
short_wald_summary: Summary of short-run Wald test results.
short_hypotheses: Textual description of short-run null and alternative hypotheses.
long_wald_tests: Detailed long-run Wald test results.
short_wald_tests: Detailed short-run Wald test results.
vars: Variables included in the symmetry test.
type: Type of symmetry test.
call: Original function call that produced the object.

component and variable arguments allow for further subsetting of the symmetry test results, enabling users to extract specific components (long-run or short-run) and/or results for specific variables of interest.

Examples

kardl_model <- kardl(DriversKilled ~ asym(PetrolPrice + drivers),
  data = Seatbelts,
  mode = c(2, 1, 0, 4, 0)
)

# Examples of extracting components from a fitted kardl_lm model object
# kardl_extract(kardl_model, what = "data_ts_info")
kardl_extract(kardl_model, what = "data_is_ts")
#> [1] TRUE
kardl_extract(kardl_model, what = "data_class")
#> [1] "mts"    "ts"     "matrix" "array" 
kardl_extract(kardl_model, what = "data_start")
#> [1] 1969    1
kardl_extract(kardl_model, what = "data_end")
#> [1] 1984   12
kardl_extract(kardl_model, what = "data_frequency")
#> [1] 12
kardl_extract(kardl_model, what = "data_deltat")
#> [1] 0.08333333
kardl_extract(kardl_model, what = "data_tsp")
#> [1] 1969.000 1984.917   12.000
head(kardl_extract(kardl_model, what = "data_time"))
#>           Jan      Feb      Mar      Apr      May      Jun
#> 1969 1969.000 1969.083 1969.167 1969.250 1969.333 1969.417
kardl_extract(kardl_model, what = "no_constant")
#> [1] FALSE
kardl_extract(kardl_model, what = "trend")
#> [1] FALSE
kardl_extract(kardl_model, what = "asym_long_vars")
#> [1] "PetrolPrice" "drivers"    
kardl_extract(kardl_model, what = "asym_short_vars")
#> [1] "PetrolPrice" "drivers"    
kardl_extract(kardl_model, what = "deterministic")
#> character(0)
kardl_extract(kardl_model, what = "dependent_var")
#> [1] "DriversKilled"
kardl_extract(kardl_model, what = "independent_vars")
#> [1] "PetrolPrice" "drivers"    
kardl_extract(kardl_model, what = "all_vars")
#> [1] "DriversKilled" "PetrolPrice"   "drivers"      
kardl_extract(kardl_model, what = "all_asym_vars")
#> [1] "PetrolPrice" "drivers"    
kardl_extract(kardl_model, what = "indep_as_excluded")
#> character(0)
kardl_extract(kardl_model, what = "indep_al_excluded")
#> character(0)
kardl_extract(kardl_model, what = "short_run_vars")
#> [1] "DriversKilled"       "asyP_PetrolPrice_PP" "asyN_PetrolPrice_NN"
#> [4] "asyP_drivers_PP"     "asyN_drivers_NN"    
kardl_extract(kardl_model, what = "long_run_vars")
#> [1] "DriversKilled"       "asyP_PetrolPrice_PP" "asyN_PetrolPrice_NN"
#> [4] "asyP_drivers_PP"     "asyN_drivers_NN"    
kardl_extract(kardl_model, what = "shortrun_length")
#> [1] 4
kardl_extract(kardl_model, what = "lag_rows_number")
#> [1] 768
kardl_extract(kardl_model, what = "model_type")
#> [1] "NN"
# kardl_extract(kardl_model, what = "data")
kardl_extract(kardl_model, what = "start_time")
#> [1] "2026-07-21 16:34:59 +03"
kardl_extract(kardl_model, what = "end_time")
#> [1] "2026-07-21 16:34:59 +03"
kardl_extract(kardl_model, what = "span")
#> Time difference of 0.002884626 secs
kardl_extract(kardl_model, what = "opt_lag")
#>       DriversKilled asyP_PetrolPrice_PP asyN_PetrolPrice_NN     asyP_drivers_PP 
#>                   2                   1                   0                   4 
#>     asyN_drivers_NN 
#>                   0 
kardl_extract(kardl_model, what = "lag_criteria")
#> NULL
kardl_extract(kardl_model, what = "all_cr_lags")
#> NULL
kardl_extract(kardl_model, what = "model_formula")
#> L0.d.DriversKilled ~ L1.DriversKilled + L1.asyP_PetrolPrice_PP + 
#>     L1.asyN_PetrolPrice_NN + L1.asyP_drivers_PP + L1.asyN_drivers_NN + 
#>     L1.d.DriversKilled + L2.d.DriversKilled + L0.d.asyP_PetrolPrice_PP + 
#>     L1.d.asyP_PetrolPrice_PP + L0.d.asyN_PetrolPrice_NN + L0.d.asyP_drivers_PP + 
#>     L1.d.asyP_drivers_PP + L2.d.asyP_drivers_PP + L3.d.asyP_drivers_PP + 
#>     L4.d.asyP_drivers_PP + L0.d.asyN_drivers_NN
#> <environment: 0x5c5f4ca2cf98>
kardl_extract(kardl_model, what = "k")
#> [1] 17
kardl_extract(kardl_model, what = "n")
#> [1] 186


# Examples of extracting components from a kardl_mplier object
# \donttest{
# This long-running example won't be tested by CRAN
m <- mplier(kardl_model, horizon = 40)
head(kardl_extract(m, what = "multipliers"))
#>      h asyP_PetrolPrice_PP asyN_PetrolPrice_NN PetrolPrice_dif asyP_drivers_PP
#> [1,] 0         -352.594515         -1036.63715     -1389.23167      0.07657003
#> [2,] 1         -128.915185            18.50466      -110.41053      0.09324058
#> [3,] 2            7.693244           138.57168       146.26492      0.05889118
#> [4,] 3           30.569985           166.80773       197.37771      0.07772017
#> [5,] 4            6.221079            77.68728        83.90836      0.07421712
#> [6,] 5           -7.338373            61.43681        54.09843      0.07541212
#>      asyN_drivers_NN   drivers_dif
#> [1,]     -0.07535387  0.0012161619
#> [2,]     -0.08524853  0.0079920489
#> [3,]     -0.08146087 -0.0225696959
#> [4,]     -0.07468890  0.0030312672
#> [5,]     -0.07373551  0.0004816077
#> [6,]     -0.07437305  0.0010390648
kardl_extract(m, what = "omega")
#> [1]  0.05704919 -0.05775601 -0.07941748
head(kardl_extract(m, what = "lambda"))
#>      asyP_PetrolPrice_PP asyN_PetrolPrice_NN asyP_drivers_PP asyN_drivers_NN
#> [1,]           -352.5945            1036.637     0.076570029     0.075353867
#> [2,]            243.7946           -1114.281     0.012302290     0.005595784
#> [3,]            103.4832               0.000    -0.030878063     0.000000000
#> [4,]              0.0000               0.000     0.027832419     0.000000000
#> [5,]              0.0000               0.000    -0.005237174     0.000000000
#> [6,]              0.0000               0.000    -0.000245614     0.000000000
kardl_extract(m, what = "horizon")
#> [1] 40

# Examples of extracting components from a kardl_boot object
boot_results <- bootstrap(kardl_model, horizon = 40, replications = 2)
head(kardl_extract(boot_results, what = "multipliers"))
#>   h asyP_PetrolPrice_PP asyN_PetrolPrice_NN PetrolPrice_dif asyP_drivers_PP
#> 1 0         -352.594515         -1036.63715     -1389.23167      0.07657003
#> 2 1         -128.915185            18.50466      -110.41053      0.09324058
#> 3 2            7.693244           138.57168       146.26492      0.05889118
#> 4 3           30.569985           166.80773       197.37771      0.07772017
#> 5 4            6.221079            77.68728        83.90836      0.07421712
#> 6 5           -7.338373            61.43681        54.09843      0.07541212
#>   asyN_drivers_NN   drivers_dif PetrolPrice_CI_upper PetrolPrice_CI_lower
#> 1     -0.07535387  0.0012161619         -2352.949800          -2518.00455
#> 2     -0.08524853  0.0079920489          -335.193323           -466.44350
#> 3     -0.08146087 -0.0225696959           302.096843            -23.46873
#> 4     -0.07468890  0.0030312672           369.427212            -99.46575
#> 5     -0.07373551  0.0004816077             8.623113           -184.60401
#> 6     -0.07437305  0.0010390648           -96.039434           -191.78185
#>   drivers_CI_upper drivers_CI_lower
#> 1      0.011152324    -0.0072414082
#> 2      0.003340704    -0.0086973935
#> 3     -0.009304857    -0.0203781998
#> 4      0.021895374     0.0084294684
#> 5      0.007754456     0.0009541362
#> 6      0.002461729    -0.0013641216
kardl_extract(boot_results, what = "level")
#> [1] 95
kardl_extract(boot_results, what = "replications")
#> [1] 2
kardl_extract(boot_results, what = "horizon")
#> [1] 40
# }
# Examples of extracting components from a kardl_test object
test_results <- psst(kardl_model)
kardl_extract(test_results, what = "type")
#> [1] "cointegration"
kardl_extract(test_results, what = "case")
#> [1] 3
kardl_extract(test_results, what = "statistic")
#>         t 
#> -9.917541 
kardl_extract(test_results, what = "method")
#> [1] "Pesaran-Shin-Smith (PSS) Bounds t-test for cointegration"
kardl_extract(test_results, what = "alternative")
#> [1] "Cointegrating relationship exists"
kardl_extract(test_results, what = "data.name")
#> [1] "model"
kardl_extract(test_results, what = "sample.size")
#> [1] 191
kardl_extract(test_results, what = "hypotheses")
#> Hypotheses:
#> 
#> H0: Coef(L1.DriversKilled) = 0 
#> H1: Coef(L1.DriversKilled) ≠ 0 
kardl_extract(test_results, what = "var_names")
#> [1] "L1.DriversKilled"
kardl_extract(test_results, what = "k")
#> [1] 4
kardl_extract(test_results, what = "n")
#> [1] 192
kardl_extract(test_results, what = "sig")
#> [1] "auto"
kardl_extract(test_results, what = "notes")
#> NULL

# Examples of extracting components from a kardl_test_summary object
test_summary <- summary(test_results)
kardl_extract(test_summary, what = "statistic")
#>         t 
#> -9.917541 
kardl_extract(test_summary, what = "case")
#> [1] "III"
kardl_extract(test_summary, what = "variables")
#> [1] "L1.DriversKilled"
kardl_extract(test_summary, what = "decision")
#> [1] "Reject H0 → Cointegration (at 1% level)"
kardl_extract(test_summary, what = "hypotheses")
#> Hypotheses:
#> 
#> H0: Coef(L1.DriversKilled) = 0 
#> H1: Coef(L1.DriversKilled) ≠ 0 
kardl_extract(test_summary, what = "numeric_decision")
#> [1] 1
kardl_extract(test_summary, what = "significance_level")
#> [1] "0.01"
kardl_extract(test_summary, what = "critical_values")
#>           L     U
#> 0.10  -2.57 -3.66
#> 0.05  -2.86 -3.99
#> 0.025 -3.13 -4.26
#> 0.01  -3.43 -4.60
kardl_extract(test_summary, what = "k")
#> [1] 4
kardl_extract(test_summary, what = "notes")
#> NULL

# Examples of extracting components from a kardl_symmetric object
symmetry_results <- symmetrytest(kardl_model)
kardl_extract(symmetry_results, what = "long_wald_summary")
#> Long-run:
#>             Df Sum of Sq Mean Sq F value Pr(>F)
#> PetrolPrice  1    33.094  33.094  0.2587 0.6117
#> drivers      1    19.680  19.680  0.1538 0.6954
kardl_extract(symmetry_results, what = "long_hypotheses")
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: - Coef(L1.asyP_PetrolPrice_PP)/Coef(L1.DriversKilled) = - Coef(L1.asyN_PetrolPrice_NN)/Coef(L1.DriversKilled) 
#> H1: At least one coefficient differs from zero. 
#> 
#> 
#> Variable: drivers 
#> H0: - Coef(L1.asyP_drivers_PP)/Coef(L1.DriversKilled) = - Coef(L1.asyN_drivers_NN)/Coef(L1.DriversKilled) 
#> H1: At least one coefficient differs from zero. 
#> 
kardl_extract(symmetry_results, what = "short_wald_summary")
#> Short-run:
#>             Df Sum of Sq Mean Sq F value Pr(>F)
#> PetrolPrice  1    203.66  203.66  1.5919 0.2088
#> drivers      1      7.90    7.90  0.0618 0.8041
kardl_extract(symmetry_results, what = "short_hypotheses")
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) = Coef(L0.d.asyN_PetrolPrice_NN) 
#> H1: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) ≠ Coef(L0.d.asyN_PetrolPrice_NN) 
#> 
#> 
#> Variable: drivers 
#> H0: Coef(L0.d.asyP_drivers_PP) + Coef(L1.d.asyP_drivers_PP) + Coef(L2.d.asyP_drivers_PP) + Coef(L3.d.asyP_drivers_PP) + Coef(L4.d.asyP_drivers_PP) = Coef(L0.d.asyN_drivers_NN) 
#> H1: Coef(L0.d.asyP_drivers_PP) + Coef(L1.d.asyP_drivers_PP) + Coef(L2.d.asyP_drivers_PP) + Coef(L3.d.asyP_drivers_PP) + Coef(L4.d.asyP_drivers_PP) ≠ Coef(L0.d.asyN_drivers_NN) 
#> 
kardl_extract(symmetry_results, what = "long_wald_tests")
#> $PetrolPrice
#> 
#> 	Wald F test of a restriction on model parameters
#> 
#> data:  kardl_model
#> F = 0.25868, df1 = 1, df2 = 169, p-value = 0.6117
#> 
#> 
#> $drivers
#> 
#> 	Wald F test of a restriction on model parameters
#> 
#> data:  kardl_model
#> F = 0.15383, df1 = 1, df2 = 169, p-value = 0.6954
#> 
#> 
kardl_extract(symmetry_results, what = "short_wald_tests")
#> $PetrolPrice
#> 
#> Linear hypothesis test:
#> L0.d.asyP_PetrolPrice_PP  + L1.d.asyP_PetrolPrice_PP - L0.d.asyN_PetrolPrice_NN = 0
#> 
#> Model 1: restricted model
#> Model 2: L0.d.DriversKilled ~ L1.DriversKilled + L1.asyP_PetrolPrice_PP + 
#>     L1.asyN_PetrolPrice_NN + L1.asyP_drivers_PP + L1.asyN_drivers_NN + 
#>     L1.d.DriversKilled + L2.d.DriversKilled + L0.d.asyP_PetrolPrice_PP + 
#>     L1.d.asyP_PetrolPrice_PP + L0.d.asyN_PetrolPrice_NN + L0.d.asyP_drivers_PP + 
#>     L1.d.asyP_drivers_PP + L2.d.asyP_drivers_PP + L3.d.asyP_drivers_PP + 
#>     L4.d.asyP_drivers_PP + L0.d.asyN_drivers_NN
#> 
#>   Res.Df   RSS Df Sum of Sq      F Pr(>F)
#> 1    170 21824                           
#> 2    169 21620  1    203.66 1.5919 0.2088
#> 
#> $drivers
#> 
#> Linear hypothesis test:
#> L0.d.asyP_drivers_PP  + L1.d.asyP_drivers_PP  + L2.d.asyP_drivers_PP  + L3.d.asyP_drivers_PP  + L4.d.asyP_drivers_PP - L0.d.asyN_drivers_NN = 0
#> 
#> Model 1: restricted model
#> Model 2: L0.d.DriversKilled ~ L1.DriversKilled + L1.asyP_PetrolPrice_PP + 
#>     L1.asyN_PetrolPrice_NN + L1.asyP_drivers_PP + L1.asyN_drivers_NN + 
#>     L1.d.DriversKilled + L2.d.DriversKilled + L0.d.asyP_PetrolPrice_PP + 
#>     L1.d.asyP_PetrolPrice_PP + L0.d.asyN_PetrolPrice_NN + L0.d.asyP_drivers_PP + 
#>     L1.d.asyP_drivers_PP + L2.d.asyP_drivers_PP + L3.d.asyP_drivers_PP + 
#>     L4.d.asyP_drivers_PP + L0.d.asyN_drivers_NN
#> 
#>   Res.Df   RSS Df Sum of Sq      F Pr(>F)
#> 1    170 21628                           
#> 2    169 21620  1    7.8998 0.0618 0.8041
#> 
kardl_extract(symmetry_results, what = "vars")
#> [1] "PetrolPrice" "drivers"    
kardl_extract(symmetry_results, what = "type")
#> [1] "F"
kardl_extract(symmetry_results, what = "call")
#> symmetrytest.kardl_lm(kardl_model = kardl_model)

# Example of extracting specific components from symmetry test results
kardl_extract(symmetry_results,
  what = "short_wald_tests",
  variable = "PetrolPrice"
)
#> 
#> Linear hypothesis test:
#> L0.d.asyP_PetrolPrice_PP  + L1.d.asyP_PetrolPrice_PP - L0.d.asyN_PetrolPrice_NN = 0
#> 
#> Model 1: restricted model
#> Model 2: L0.d.DriversKilled ~ L1.DriversKilled + L1.asyP_PetrolPrice_PP + 
#>     L1.asyN_PetrolPrice_NN + L1.asyP_drivers_PP + L1.asyN_drivers_NN + 
#>     L1.d.DriversKilled + L2.d.DriversKilled + L0.d.asyP_PetrolPrice_PP + 
#>     L1.d.asyP_PetrolPrice_PP + L0.d.asyN_PetrolPrice_NN + L0.d.asyP_drivers_PP + 
#>     L1.d.asyP_drivers_PP + L2.d.asyP_drivers_PP + L3.d.asyP_drivers_PP + 
#>     L4.d.asyP_drivers_PP + L0.d.asyN_drivers_NN
#> 
#>   Res.Df   RSS Df Sum of Sq      F Pr(>F)
#> 1    170 21824                           
#> 2    169 21620  1    203.66 1.5919 0.2088
kardl_extract(symmetry_results,
  what = "long_wald_tests",
  variable = "PetrolPrice"
)
#> 
#> 	Wald F test of a restriction on model parameters
#> 
#> data:  kardl_model
#> F = 0.25868, df1 = 1, df2 = 169, p-value = 0.6117
#> 
kardl_extract(symmetry_results,
  what = "long_hypotheses",
  variable = "PetrolPrice"
)
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: - Coef(L1.asyP_PetrolPrice_PP)/Coef(L1.DriversKilled) = - Coef(L1.asyN_PetrolPrice_NN)/Coef(L1.DriversKilled) 
#> H1: At least one coefficient differs from zero. 
#> 
kardl_extract(symmetry_results,
  what = "short_hypotheses",
  variable = "PetrolPrice"
)
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) = Coef(L0.d.asyN_PetrolPrice_NN) 
#> H1: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) ≠ Coef(L0.d.asyN_PetrolPrice_NN) 
#> 
kardl_extract(symmetry_results, what = "short_hypotheses", component = "H0")
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) = Coef(L0.d.asyN_PetrolPrice_NN) 
#> 
#> Variable: drivers 
#> H0: Coef(L0.d.asyP_drivers_PP) + Coef(L1.d.asyP_drivers_PP) + Coef(L2.d.asyP_drivers_PP) + Coef(L3.d.asyP_drivers_PP) + Coef(L4.d.asyP_drivers_PP) = Coef(L0.d.asyN_drivers_NN) 
kardl_extract(symmetry_results, what = "short_hypotheses", component = "H1")
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H1: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) ≠ Coef(L0.d.asyN_PetrolPrice_NN) 
#> 
#> 
#> Variable: drivers 
#> H1: Coef(L0.d.asyP_drivers_PP) + Coef(L1.d.asyP_drivers_PP) + Coef(L2.d.asyP_drivers_PP) + Coef(L3.d.asyP_drivers_PP) + Coef(L4.d.asyP_drivers_PP) ≠ Coef(L0.d.asyN_drivers_NN) 
#> 

kardl_extract(symmetry_results,
  what = "short_hypotheses",
  variable = "PetrolPrice", component = "H0"
)
#> Hypotheses:
#> 
#> 
#> Variable: PetrolPrice 
#> H0: Coef(L0.d.asyP_PetrolPrice_PP) + Coef(L1.d.asyP_PetrolPrice_PP) = Coef(L0.d.asyN_PetrolPrice_NN)

Usage

Arguments

Value

Details

Supported classes

Components for kardl_lm objects

Components for kardl_mplier objects

Components for kardl_boot objects

Components for kardl_test objects

Components for kardl_test_summary objects