View source: R/auto_case_ardl.R
auto_case_ardl | R Documentation |
This function finds the best ARDL model specification and conduct bounds test by relying on the general to specific approach.
auto_case_ardl(x, dep_var, expl_var, p_order, q_order,
gets_pval = 0.05, order_l = 3, graph_save = FALSE)
x |
Dataframe. |
dep_var |
A Character vector that contain the response variable. |
expl_var |
Character vector containing the list of explanatory variable(s). |
p_order |
An integer. Lag differenced adopted for the differenced response variable |
q_order |
An integer. Lag differenced adopted for the differenced explanatory variable(s) |
gets_pval |
The p- value which served as the criteria for eliminating non-significant variable in the course of obtaining the best model based on the Schwarz information criteria. |
order_l |
Integer. Needed for the autocorrelation and heteroscedasticity test |
graph_save |
Logical. If TRUE, displays the stability plots |
The procedure of the general-to-specific approach in obtaining the parsimonious model involves conducting the multi-path backwards elimination; tests both single and multiple hypothesis tests, diagnostics tests and goodness-of-fit measures. See page 5 of Sucarrat, (2021) for more details.
The value for gets_pval is influential the final model based on the multipath backward elimination. For more details on the general-to-specific approach, see the vignette of the 'gets' package.
Parsimonious_ARDL_fit |
Return an estimated general-to-specific ARDL model |
Parsimonious_ECM_fit |
Return an estimated general-to-specific error correction model |
Summary_ecm_fit |
Return the summary of 'Parsimonious_ECM_fit' |
Parsimonious_ECM_diagnostics_test |
Return the diagnostic test for 'Parsimonious_ECM_fit'.The diagnostic tests items are the Breusch-Godfrey test for higher-order serial correlation (BG_SC_lm_test). The Engle (1982) test for conditional heteroscedasticity (LM_ARCH_test). The test for non-normality is that of Jarque and Bera (1980). The RESET null hypothesis adopted implies - including the 2nd - degree terms improve the fit (over the model specified). Ljung and Box (1978) tests for autocorrelation in the residuals |
cointegration |
Return the F statistic, the upper and lower critical values for PSS (2001) bounds test |
Longrun_relation |
The estimated longrun relation from the error correction model |
Do not differenced the variables to be adopted in this function and all other functions for ARDL and NARDL estimation. The package inherently takes the difference and produced output with a prefix (D.) to the variable name and suffix the variable name with underscore (_) and the lag value.
Sucarrat, G. User-Specified General-to-Specific (GETS) and Indicator Saturation (ISAT) Methods. 28th September 2021. https://mirror.epn.edu.ec/CRAN/web/packages/gets/vignettes/user-defined-gets-and-isat.pdf
gets
gets_nardl_uecm
ardl_uecm
nardl_auto_case
data("expectation")
out_aut <- auto_case_ardl(x = expectation,
dep_var = 'n12m_inf_exp',
expl_var = c('food_inf',"hawkish","dovish"),
p_order = 2,
q_order = c(4,4,4),
gets_pval = 0.05,
graph_save = FALSE)
data("fuel_price")
out_aut <- auto_case_ardl(x = fuel_price,
dep_var = 'fpp',
expl_var = c('bdc','wti'),
p_order = 2,
q_order = c(4,4),
gets_pval = 0.08,
graph_save = TRUE)
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