gets_ardl_uecm {ardl.nardl} | R Documentation |

Adopt the general-to-specific approach to estimate the autoregressive distributed lag model

```
gets_ardl_uecm(x, dep_var, expl_var, p_order = c(2), q_order = c(3),
gets_pval = 0.1, case = 3, F_HC = FALSE, order_l = 5,
graph_save = FALSE)
```

`x` |
data frame |

`dep_var` |
A character vector. The dependent variable. |

`expl_var` |
Character vector. List of explanatory variable(s) |

`p_order` |
Integer. Maximum number of lags for 'dep_var' |

`q_order` |
Integer. Maximum number of lags for 'expl_var' |

`gets_pval` |
Integer value between 0 and 1 needed for the general-to-specific approach. The default is 0.1 (10 percent significance level). The chosen p-value is the criteria for determining non-significant repressors to be eliminated in a backward elimination path. The final parsimonious model is the best fit model based on the Schwarz information criteria |

`case` |
Positive integer 1 to 5. Default is 3 |

`F_HC` |
Logical (default is FALSE). If TRUE, Heteroscedasticity-Consistent Covariance Matrix Estimation is applied to the model before when estimating F statistic |

`graph_save` |
Logical. If TRUE, display stability plot. Default is FALSE |

`order_l` |
Integer. order for the serial correlation, and heteroscedasticity test |

`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 |

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations. Econometrica (50) 987 - 1007

Ljung GM, Box GEP (1978). On a Measure of Lack of Fit in Time Series Models. Biometrika, 65(2), 297 - 303. https://doi.org/10.2307/2335207

Jarque C, Bera A (1980). Efficient Tests for Normality, Homoskedasticity, and Serial Independence. Economics Letters, 6(3), 255 - 259. https://doi.org/10.1016/0165-1765 (80) 90024-5

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships Journal of applied econometrics, 16(3), 289-326

```
data(expectation)
out <- gets_ardl_uecm(x = expectation,
dep_var = c('nq_inf_exp'),
expl_var = c('food_inf','nethawkish'),
p_order = c(4),
q_order = c(5,7),
gets_pval = 0.1,
case = 4,
graph_save = FALSE,
F_HC = FALSE,
order_l = 7)
out
```

[Package *ardl.nardl* version 1.2.3 Index]