Table 1. Stationarity for each variable...

Table 1.

Stationarity for each variable

Variables	Fisher-type tests								IPS test				Hadri-LM
	Fisher-ADF statistic				Fisher-PP statistic				t-bar	t-tilde-bar	Z-t-tilde-bar	W-t-bar
	P	Z	L*	Pm	P	Z	L*	Pm	t-bar	t-tilde-bar	Z-t-tilde-bar	W-t-bar	z
GDPper capita	202.6674*	−11.5662*	−14.5066*	22.2913*	221.3604*	−11.9751*	−15.8329*	24.7045*	−4.1019*	−3.1545*	−8.5224*	−10.6548*	−0.2522
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.5996)
d(GDPpercapita)													2.9328*
(p-value)													(0.0017)
ln(FDI)	89.3351*	−5.8603*	−6.1239*	7.6601*	27.3047	0.1731	0.0924	−0.3480	−1.5241	−1.4407	−0.0560	−0.1644	44.8990*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.6072)	(0.5687)	(0.5367)	(0.6361)	(0.4777)	(0.4777)	(0.4777)	(0.4347)	(0.0000)
dln(FDI)					335.3995*	−14.8545*	−23.9708*	39.4269*	−4.8462*	−3.3364*	−9.4584*	−11.4534*
(p-value)					(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
ln(TR)	99.0820*	−6.6697*	−6.9526*	8.9185*	55.1164*	−1.9327**	−2.5032*	3.2425*	−1.9643	−1.7373	−1.5213	−2.2096**	38.3547*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0034)	(0.0266)	(0.0072)	(0.0006)	(−2.2096)	(−2.2096)	(−2.2096)	(0.0136)	(0.0000)
ODA	163.3270*	−9.3024*	−11.5795*	17.2124*	159.2573*	−7.0288*	−10.6775*	16.6870*	−3.1956*	−2.4862*	−5.2207*	−6.5801*	25.4168*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)

Variables	Fisher-type tests								IPS test				Hadri-LM
	Fisher-ADF statistic				Fisher-PP statistic				t-bar	t-tilde-bar	Z-t-tilde-bar	W-t-bar
	P	Z	L*	Pm	P	Z	L*	Pm	t-bar	t-tilde-bar	Z-t-tilde-bar	W-t-bar	z
GDPper capita	202.6674*	−11.5662*	−14.5066*	22.2913*	221.3604*	−11.9751*	−15.8329*	24.7045*	−4.1019*	−3.1545*	−8.5224*	−10.6548*	−0.2522
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.5996)
d(GDPpercapita)													2.9328*
(p-value)													(0.0017)
ln(FDI)	89.3351*	−5.8603*	−6.1239*	7.6601*	27.3047	0.1731	0.0924	−0.3480	−1.5241	−1.4407	−0.0560	−0.1644	44.8990*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.6072)	(0.5687)	(0.5367)	(0.6361)	(0.4777)	(0.4777)	(0.4777)	(0.4347)	(0.0000)
dln(FDI)					335.3995*	−14.8545*	−23.9708*	39.4269*	−4.8462*	−3.3364*	−9.4584*	−11.4534*
(p-value)					(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
ln(TR)	99.0820*	−6.6697*	−6.9526*	8.9185*	55.1164*	−1.9327**	−2.5032*	3.2425*	−1.9643	−1.7373	−1.5213	−2.2096**	38.3547*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0034)	(0.0266)	(0.0072)	(0.0006)	(−2.2096)	(−2.2096)	(−2.2096)	(0.0136)	(0.0000)
ODA	163.3270*	−9.3024*	−11.5795*	17.2124*	159.2573*	−7.0288*	−10.6775*	16.6870*	−3.1956*	−2.4862*	−5.2207*	−6.5801*	25.4168*
(p-value)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)

Notes:

The t-tilde-bar $(\tilde{t} - ba r_{NT})$ statistic is similar to the t-bar (t – bar_NT), statistic except a different error variance estimator of the Dickey-Fuller regression is used. A standardized version of the statistic t-tilde-bar is $- t - tilde - bar (Z_{\tilde{t} - bar})$ ⁠. In presence of serial correlation, Dickey-Fuller regression is augmented as follow: $Δ y_{i t} = ϕ_{i} y_{i, t - 1} + z_{i t}^{'} γ_{i} + \sum_{j = 1}^{p} Δ y_{i, t - j} + ϵ_{i, t}$ where p is the number of lags. Im et al. (1997) propose thus, another statistic noted W_t–bar, which follows an asymptotical standard normal distribution when T → ∞ followed by N → ∞; *, ** et *** indicates the significance at 1%, 5% et 10%

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