gets_nardl_uecm {ardl.nardl} R Documentation

## Parsimonious model for the autoregressive distributed lag model

### Description

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

### Usage

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)

### Arguments

 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

### Value

 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

### References

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.

gets_ardl_uecm ardl_uecm

### Examples

## 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]