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. 2023 Jun 20;9(6):e17417. doi: 10.1016/j.heliyon.2023.e17417

Table 5.

Cointegrating long-run estimations using the Autoregressive Distributive Lag (ARDL) model.

Panel A: Variables Model 1 (1, 0, 3, 1) Model 2 (2, 2, 1, 1, 3)
LY 0.86*** (0.09)
LRP −0.26** (0.10) 0.34* (0.17)
LR −0.015*** (0.004)
LX 1.01*** (0.23)
LFCG −0.96*** (0.29)
LI −0.28 (0.22)
Trend 0.02*** (0.01) 0.02*** (0.01)
C
−1.10 (1.10)
14.70 (3.72)
Panel B: Cointegration test through the F-Bound test
Statistics values 9.36 10.54

Critical values
I(0) I(1)
5%: 3.16 4.20
1%: 4.43 5.82
I(0) I(1)
5%: 4.04 4.51
1%: 5.60 7.17
Diagnostic tests Observations = 39
R2 = 0.98
DW = 2.08
SER = 0.02
Normality = X2 0.83 (p. 0.66)
LM = 0.29 (p. 0.86)
Hetero. = 12.40 (p. 0.25)
ARCH = 0.09 (p. 0.95)
Ramsey-reset = 0.30 (p. 0.58)
Observations = 38
R2 = 0.99
DW = 1.94
SER = 0.01
Normality = X2 0.49 (p. 0.78)
LM = 3.42 (p. 0.33)
Hetero. = 13.99 (p. 0.52)
ARCH = 5.52 (p. 0.14)
Ramsey-reset = 0.40 (p. 0.53)

Notes: Models run with automated lag selection process in the statistical software (Eviews 10), P = probability.