ardl.nardl (version 1.3.0)

auto_case_ardl: Obtain the best ARDL model specification and bounds test.

Description

This function finds the best ARDL model specification and conduct bounds test by relying on the general to specific approach.

Usage

auto_case_ardl(x, dep_var, expl_var, p_order, q_order, 
gets_pval = 0.05, order_l = 3, graph_save = FALSE)

Value

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

Arguments

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

Details

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.

References

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

See Also

gets gets_nardl_uecm ardl_uecm nardl_auto_case

Examples

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)