Asymmetric effect of real effective exchange rate and Saudi non-oil export determinants

Figures & data

Figure 1. Oil and non-oil Saudi exports.

Source: Created by the author; data are adapted from the World Bank database.

Table 1. Variables Description

Symbol	Variable name	Definition	Source	Covered period
NXg_YD	Non-oil exports goods based on the real measures	Non-oil exports (defeated by non-oil deflator) divided by real GDP at constant price of 2010 in Million Riyals	SAMA	1984–2020
NXg_Y	Non-oil exports goods based on the current measures	Non-oil exports divided by GDP at current price in Million Riyals
REER	Real effective exchange rate	Real effective exchange rate index (2010 = 100)	World Bank
YF	Foreign income	GDP of the rest of the word at constant price index of 2015 product by 100 (%100)
XP_XPF	Relative price	Relative prices of export unit value index (2015 = 100) of the local to the 10 top trade partners of Saudi
DPC	Domestic production capacity	GDP at Constant Prices (2010 = 100) (Million Riyals)	SAMA
OPNX	Non-oil openness index	Non-oil export plus total import divided by real GDP (Million Riyals)	Calculated using SAMA data base.
IM_Y	Import of merchandises as percentage of GDP	Total imports except of army goods (% of GDP) measured in Millions of Riyals
CR_Y	Domestic credit to private sector	Sum of bank claims on private sector and investments in private securities, divided by GDP at the Current price all in Million Riyals. (Its same series of domestic credit to private sector as percentage of GDP provided in WB).
GOV_Y	Government expenditure	General government final consumption expenditure as percentage of current GDP in Million Riyals.
INVT_Y	Capital	gross fixed capital formation divided by GDP deflator in Million Riyals.
IHC	Human Capital	Index of Human Capital per Person for Saudi Arabia, Index, Annual, Not Seasonally Adjusted	Penn World Table (Feenstra et al., 2015)	1984–2019

Table 2. Stability test of variables

Variables	Equation	ADF		ADF breakpoint
		Degree of the significant	Statistic	Degree of the significant	break point
LNXG_YD	Intercept	At the first difference	−3.9***	At the level	2000	−4.66**
	Trend & intercept		−3.9***	—	—	—
LNXg_Y	Intercept	—	—	At the level	2000	−8.6***
	Trend & intercept	At the level	−4.57***		2002	−5.73***
LREER	Intercept	At the level	−5.17***	—	—	—
	Trend & intercept		−4.63***	At the level	2014	−5.54**
LYF	Intercept	At the first difference	−3.23**	At the first difference	2009	−7.83***
	Trend & intercept		−3.42**			−6.08***
LDPC	Intercept	At the first difference	−7.98***	At the first difference	2016	−8.35***
	Trend & intercept		−8.19***		2002	−10.00***
LINV_P	Intercept	At the first difference	−6.51***	—	—	—
	Trend & intercept		−6.38***	—	—	—
LINVT_Y	Intercept	At the first difference	−6.54***	—	—	—
	Trend & intercept		−6.48***	—	—	—
LGOV_Y	Intercept	At the first difference	−6.47***	—	—	—
	Trend & intercept		−6.51***	—	—	—
LIM_Y	Intercept	At the level	−3.40***	—	—	—
	Trend & intercept		−3.25*	—	—	—
LXP_XPF	Intercept	At the first difference	−4.96***	—	—	—
	Trend & intercept		−4.77***	—	—	—
LCR_Y	Intercept	—	—	—	—	—
	Trend & intercept	At the level	−4.40***	—	—	—
IHC	Intercept	—	—	—	—	—
	Intercept Trend & intercept	At the level	−3.55**	—	—	—
LIHC	Intercept	—	—	—	—	—
	Intercept Trend & intercept	At the first difference	−4.38**	—	—	—
LOPNX	Intercept	At the first difference	−6.38***	—	—	—
	Intercept Trend & intercept		−6.24***	—	—	—

Notes: ADF and ADF breakpoints are the reduced forms of Augmented Dickey—Fuller and Dickey—Fuller breakpoints, respectively. *, **, and*** refer to the significance at the 1, 5, and 10 percent levels correspondingly. The optimal lags are chosen using the SIC criterion (information Schwarz).

Figure 2. Scatter plot of the relationships between LNXG and LREER, LREER_NEG and LREER_POS.

Table 3. OLS and threshold regression of equation 1 (demand function of exports)

Column	1	2		3
Dependent variable Threshold variable Sample Method	LNXg_Y LREER 1984 2019 OLS	LNXg_Y LREER 1984 2019 Threshold regression^2		LNXg_Y LREER 1990 2020 Smooth threshold^3
Consonant	0.667 (0.512)	Below Consonant LREER	3.87*** (3.80) −0.66*** (−3.31)	Below Consonant LREER	3.50 (3.55) −0.52*** (−2.54)
		Thr.V.	4.91	Thr. V.	4.74
LREER	−0.062 (−0.25)	Above Consonant LREER	7.00*** (3.96) −1.22*** (−3.65)	Consonant LREER	48.83*** (6.40) −10.05*** (−6.30)
				Thr. V.	4.81
				Consonant LREER	289.40 (3.54) −59.94***(−4.68)
				Thr. V.	4.82
				Above Consonant LREER	−29.34***(−2.54) 6.23***(2.61)
LNXG_Yt-1	0.79*** 8.90	0.31*** 2.84		0.12(1.39)
TREND	——-	0.020*** 0.00		0.022*** (7.02)
Adjusted R-squared	0.89	0.95		0.97
Durbin-Watson stat	1.21	1.98		1.99
F-statistic	146.68	153.38		164.22 (0.00)
Serial Correlation: Breusch-Godfrey serial correlation LM	F(2,3,1) 3.82 P.: 0.03	F-statistic: 0.142 P.: 0.86		Prob. F(2,19) P: 0.96
Heteroskedasticity Breusch-pagan LM	3.056 P: 0.06	2.409 P. : 0.05		Prob. F(9,21) P: 0.83
Specification −1 lag Ramsey test df (2, 28)	0.02 P: 0.00	0.97 P: 0.38		F-statistic 0.76 P: 0.56

*, **, and*** refer to the significance at the 1, 5, and 10 percent levels, respectively. “Prob.” or “P” refers to the probability value. TREND is a trend indicator. The numbers in parentheses () at the beginning of the table are t-statistic values. Thr. V. denotes the means threshold value.

Figure 3. CUSUM and CUSUM of squares tests of OLS regression of equations 1, table 3, Column 2.

Table 4. ARDL and NARDL regression of the demand function of non-oil exports (equation 1)

Column	1				2
Estimation Method: Dependent Variable: Sample: Lag selected:	ARDL LNXg_YD 1984 2020 ARDL (2, 4, 1)				NARDL LNXg_YD 1988 2020 ARDL (3, 3, 3, 0, 1)
Short Run Model	Coefficient		Prob.		Short Run Model	Coefficient			Prob.
dLNXG_YD t-1	0.32**		0.0231		dLNXG_YDt-1	0.36**			0.01
dLREER)	−1.20***		0.0084		dLNXG_YD t-2	0.42**			0.01
dLREER t-1	−0.16		0.7040		dLREER_POS	−3.12***			0.00
dLREER t-2	−0.54		0.1231		dLREER_POS t-1	2.32**			0.02
dLREER t-3	−0.52		0.1259		dLREER_POS t-2	2.92***			0.005
dLYF	3.0**		0.0119		dLREER_NEG	−0.40			0.55
C	−22.53***		0.0001		dLREER_NEG t-1	−1.24*			0.05
ECT	−0.52***		0.0001		dLREER_NEG t-2	−1.25***			0.00
					LYF_POS	0.02			0.73
					dLYFNEG	4.58			0.11
					C	0.019			0.71
					ECT	−0.86***			0.000
Long Run Model	Coeff.		Prob.		Long Run Model	Coeff.			Prob.
LREER	−1.63***		0.0006		LREER_POS	−5.94***			0.00
LYF	1.62***		0.0000		LREER_NEG	0.86			0.52
Adj.R:	0.56				LYF_POS	4.99***			0.00
DW	2.05				LYF_NEG	−7.84			0.22
					Adj.R:	0.649			DW	2.44
Bound Test: K = 2			I(0) Bound	I(1) Bound	Bound Test: K = 4				I(0) Bound	I(1) Bound
F statistic: 6.85***	5%		3.79	4.85	F statistic: 6.59***	5%			2.86	4.01
Heteroskedasticity Breusch-pagan LM	Prob. F(9,23)0.4129				Heteroskedasticity
					Breusch-pagan LM	Prob. F(14,18)0.4889
Serial Correlation					Serial Correlation
Breusch-Godfrey serial correlation LM	Prob. F(2,21)0.7945				Breusch-Godfrey serial correlation LM test	Prob. F(2,16)0.1109
Ramsey test df(17) p:	df	Probability			Ramsey test t. df(16)		df	Probability
	22	0.3603				t-statistic	17	0.1708

*, **, and*** refer to the significance at the 1, 5, and 10 percent levels correspondingly. “Prob.” refers to the probability value.

Figure 4. CUSUM and CUSUM of squares tests of ARDL regression of equation 1, table 4.

Figure 5. CUSUM and CUSUM of squares tests of NARDL regression of equation 1, table 4.

Figure 6. Asymmetric plot of NARDL regression of equation 1.

Figure 7. Relationship between LNXg_y and LXP_XPF.

Table 5. Estimation of the supply function of non-oil exports (equation 2)

Column	1
Estimation Method: Dependent Variable: Sample: Lag selected:	ARDL LNXG_YD 1988 2020 ARDL (4, 4, 3)
Short Run Model	Coefficient		Prob.
dLNXG_YD t-1	0.52***		0.001
dLNXG_YD t-2	0.27*		0.07
dLNXG_YD t-3	0.33**		0.04
dLXP_XPF)	0.46***		0.00
dLXP_XPF t-1	−0.33***		0.01
dLXP_XPF t-2	−0.25***		0.01
dLXP_XPF t-3	−0.13		0.17
dLDPC	−0.42		0.37
dLDPC t-1	−0.51		0.25
dLDPC t-2	−0.57		0.13
C	−10.22***		0.00
ECT	−1.03***		0.00
Long Run Model	Coeff.		Prob.
LXP_XPF	0.79***		0.00
LDPC	0.54***		0.00
Adjusted R-squ.	0.59		Durbin-Watson:	2.07
Bound Test: K = 2	I0 Bound			I1 Bound
F statistic: 9.484404 5%	3.79			4.85
Heteroskedasticity: Breusch-pagan LM	Prob. F(13,19)0.86
Serial Correlation:
Breusch-Godfrey serial correlation LM	Prob. F(2,17)0.66
Specification under 1 lag Ramsey test t- statistic	df	Probability
	18	0.39

*, **, and *** refer to the significance at the 1, 5, 10 percent levels, respectively. “Prob.” refers to the probability value. ECT is the error correction term.

Figure 8. CUSUM and CUSUM of squares tests of ARDL regression of equation 2, table 5.

Figure 9. Scatter plots of LNXg_Y and LHIC, LINVT, LCR_Y, LGOV_Y, LIM_Y, OPNX.

Figure 10. CUSUM and CUSUM of squares tests of ARDL regression of equation 3, Column 1, table 6.

Figure 11. CUSUM and CUSUM of squares tests of ARDL regression of equation 3, Column 2, table 6.

Figure 12. CUSUM and CUSUM of squares tests of ARDL regression of equation 3, table 7.

Table 6. ARDL estimating the supply function of non-oil exports in equation 3

Column	1					2
Estimation Method: Dependent Variable: Sample: Lag selected:	ARDL LNXG_YD 1987 2020 ARDL(3, 0, 0, 2, 1) Whiteheteroskedasticity-consistentstandard errors & covariance					RDL LNXG_YD 1988 2020 ARDL (4, 0, 3, 3, 0)
Short Run Model	Coefficient		Prob.			Short Run Model	Coefficient			Prob.
dLNXG_YD t-1	0.28**		0.02			dLNXG_YD t-1	0.33*			0.05
dLNXG_YD t-2	0.10		0.37			dLNXG_YD t-2	0.20			0.28
LXP_XPF	-0.03		0.32			dLNXG_YD t-3	-0.00			0.97
LGOV_Y	-0.09		0.49			LXP_XPF	0.03			0.57
dLOPNX	0.76**		0.00			dLGOV_Y	-0.44*			0.05
dLOPNX t-1	0.01		0.92			dLGOV_Y t-1	0.14			0.59
dLCR_Y	-0.33**		0.02			dLGOV_Y t-2	0.05			0.83
C	-1.84**		0.01			dLIM_Y	0.16			0.46
ECT	-0.95**		0.00			dLIM_Y t-1	-0.51**			0.03
						dLIM_Y t-2	-0.26			0.31
						LCR_Y	-0.02			0.80
						C	-2.26***			0.00
						ECT	-0.74***			0.00
Long Run Model	Coeff.		Prob.			Long Run Model	Coeff.			Prob.
LXP_XPF	0.39***		0.00			LXP_XPF	0.52***			0.00
LGOV_Y	-0.63**		0.00			LGOV_Y	-0.74***			0.00
LOPNX	0.59***		0.01			LIM_Y	0.83***			0.00
LCR_Y	0.62***		0.00			LCR_Y	0.55***			0.00
Adj.R:	0.66					Adj. R:	0.52
DW	2.19					DW	2.39
F statsric	7.43					F statsric	3.56
Bound Test: K=2						Bound Test: K=4
F statistic: 9.73***	I0 Bound		I1 Bound			F statistic: 4.34***	I0 Bound			I1 Bound
5%	2.86		4.01			5%	2.86			4.01
Heteroskedasticity						Heteroskedasticity
Breusch-pagan LM	F-statistic 0.75 Prob. F(10,23):0.668					Breusch-pagan LM	F-statistic 0.73 Prob. F(14,18) 0.7164
Serial Correlation						Serial Correlation
`Breusch-Godfrey serial correlation LM	F-statistic 1.39 - Prob. F(2,21) 0.27					Breusch-Godfrey serial correlation LM test	F-statistic 1.11 Prob. F(2,16) 0.3525
Specification under 1 lag		Value		df	Probability	Specification under 1 lag		Value	df		Probability
Ramsey test df(17) p:	t-statistic	1.04		22	0.30	Ramsey test t. df(16)	t-statistic	0.016	17		0.98

*, **, and*** refer to the significance at the 1, 5, and 10 percent levels correspondingly. “Prob.” refers to the probability value.

Table 7. ARDL estimation of the supply function of non-oil exports in equation 3 with LINV and LIHC

Column
Estimation Method: Dependent Variable: Sample: Lag selected:	ARDL LNXG_YD 1987 2019 ARDL(3, 2, 0, 0) White heteroskedasticity-consistent standard errors & covariance
Short Run Model	Coefficient				Prob.
dLNXG_YD t-1	0.33**				0.04
dLNXG_YD t-2	0.18				0.27
dLINVT_Y)	−0.31				0.16
dLINVT_Y t-	−0.38*				0.09
LIHC	−0.52				0.21
LXP_XPF	0.11				0.16
C	−2.85***				0.00
ECT	−0.57***				0.00
Long Run Model	Coeff.				Prob.
LINVT_Y	0.67**				0.02
LIHC	2.05***				0.006
LXP_XPF	0.54***				0.001
Adj.R:	0.965
DW	2.14
F Sta.	4.28
Bound Test: K = 2					I0 Bound	I1 Bound
F statistic: 4.482**	5%				3.23	4.35
Heteroskedasticity test: Breusch-pagan LM	F-statistic 0.48 Prob. F(2,22) 0.62
Breusch-Godfrey serial correlation LM	F-statistic 0.64 Prob. F(8,24) 0.731
Specification under 1 lag: Ramsey test:		Value	df	Probability
	t-statistic	0.35	23	0.72

*, ** and*** refer to the significance at the 1, 5, and 10 percent levels, respectively. “Prob.” refers to the probability value.

Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. https://doi.org/10.1257/aer.20130954

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