nardl_mdv {ardl.nardl}

A NARDL model with two decomposed variables

Description

Estimate a NARDL model, having mulitple (two) decomposed variables

Usage

nardl_mdv(x, dep_var, decomp1, decomp2, thresh1 = Inf, thresh2 = Inf, 
gets = TRUE, gets_pval = 0.1, case = NULL, conservative = FALSE, p_order = c(3), 
q_order1 = c(5), q_order2 = c(5), order_l = 4, graph_save = FALSE)

Arguments

x

A dataframe

dep_var

The dependent variable

decomp1

Initial variable to be decomposed into postive and negative.

decomp2

The second variable decomposed into postive and negative.

thresh1

An integer or character vector. The threshold value (thresh1) for the first variable is adopted when computing the partial sum. Inf is the implicit threshold value for the partial sum whenever the base 'cumsum' function is adopted. The value of the threshold can be 'mean' or 0 or any other integer stated for the partial sum.

thresh2

An integer or character vector. The threshold value (thresh2) for the second decomposed variable.

gets

Logical. General-to-specific (GETS) approach. Default is TRUE which indicate adopting the GETS

gets_pval

The p-value adopted when gets is set as TRUE

case

An integer which can take either of 1, 2, 3, 4 or 5. The assumption on Bounds test procedure. Default is NULL. When it is set as an integer, gets should be set as FALSE.

conservative

Logical. Default is FALSE. When TRUE, the decomposed variables are assumed to be k = 2. When FALSE, k = 4.

p_order

An integer. Take the number of lags applicable to the dependent variable

q_order1

An integer. Take maximum number of lags applicable to the first variable

q_order2

An integer. The maximum number of lags applicable to the second variable

order_l

An integer. Used in the diagnostics test

graph_save

Logical. Default is FALSE. When TRUE, return the stability plots of the model

Details

Return a list containing

Value

NARDL_fit

NARDL model

ECM_fit

NARDL-ECM

Summary_uecm_fit

Summary of ECM_fit

ecm_diagnostics_test

Diagnostic tests

longrun_asym

longrun asymmetric test

Shortrun_asym

Shortrun asymmetric test

cointegration

PSS bounds test

Longrun_relation

Longrun relationship

Note

The decomposed variable should display both positive and negative change, preferably on a balanced scale. However, when a variable display only positive change and no negative change, vice versa, such variable should not be adopted (i.e decomposed).

References

Jordan S, Philips A (2020). _dynamac: Dynamic Simulation and Testing for Single-Equation ARDL Models_. R package version 0.1.11

Narayan, P. K. (2005). The saving and investment nexus for China: evidence from cointegration tests. Applied economics, 37(17), 1979-1990.

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.

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

Zaghdoudi, T. (2018). nardl: Nonlinear Cointegrating Autoregressive Distributed Lag Model_. R package version 0.1.5

See Also

nardl_uecm

Examples

## Not run: 
data(expectation)
nardl_mdv(x = expectation,
          dep_var = 'nq_inf_exp',
          decomp1 = 'food_inf',
          decomp2 = 'nethawkish',
          p_order = c(7),
          q_order1 = c(4),
          q_order2 = c(6),
          gets_pval = 0.1,
          conservative = FALSE,
          gets = FALSE,
          case = 5,
          order_l = 3,
          graph_save = FALSE)

## End(Not run)