auto_case_ardl {ardl.nardl} | 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)
```

[Package *ardl.nardl* version 1.2.3 Index]