gets_nardl_uecm {ardl.nardl} | R Documentation |

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

```
gets_nardl_uecm(x, decomp, dep_var, control = NULL, c_q_order = c(2),
p_order = c(3), q_order = c(4), gets_pval = 0.1, order_l = 4,
F_HC = FALSE, graph_save = FALSE, case = 3)
```

`x` |
data frame |

`decomp` |
A character vector. The variable to be decomposed to positive (pos) and negative (neg) variable. |

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

`control` |
A character vector. Default is NULL. The second dependent variable. |

`c_q_order` |
Integer. Maximum number of lags for 'control' |

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

`q_order` |
Integer. Maximum number of lags for level and differenced 'decomp' |

`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. |

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

`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. |

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

`Parsimonious_NARDL_fit ` |
Return an estimated general-to-specific NARDL model. |

`Parsimonious_ECM_fit ` |
Return an estimated general-to-specific error correction model. |

`Summary_uecm_fit ` |
Return the summary of 'Parsimonious_ECM_fit' |

`ecm_diagnostics_test ` |
Return the diagnostic test for the 'Parsimonious_ECM_fit'. The diagnostic tests indicate 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). |

`longrun_asym ` |
Return the estimated longrun asymmetric test |

`Shortrun_asym ` |
Return the estimated short-run asymmetric test.If one of the decomposed variable does not appear among the shortrun differenced variables of the parsimonious model, The value returned is a wald test of whether the sum of the coefficients of the remaining decomposed variable included does not have any significant effect on the best model |

`cointegration ` |
Return the F statistic, the upper and lower critical values for PSS (2001) bounds test. Please, disregard the tstat on the cointegration test. |

`Longrun_relation ` |
The longrun relation |

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

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.

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R., Horrace, W. (eds) Festschrift in Honor of Peter Schmidt. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-8008-3_9

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.

```
## Not run:
data(expectation)
out <- gets_nardl_uecm(x = expectation,
decomp = 'food_inf',
dep_var = 'nq_inf_exp',
control = 'nethawkish',
c_q_order = c(3),
p_order = c(3),
q_order = c(3),
gets_pval = 0.1,
graph_save = FALSE,
case = 5,
F_HC = FALSE)
out
## End(Not run)
```

[Package *ardl.nardl* version 1.2.3 Index]