Response of Ethiopian coffee price to the world coffee price: Evidence from dynamic ARDL simulations and nonlinear ARDL cointegration

Figures & data

Table 1. Summary of the empirical literature

Author (s)	Country	Method	Key findings
Gizaw et al. (2021)	Spain, France, and Norway	Threshold cointegration	Asymmetric price transmission was observed in the fresh salmon market, but price transmission does not exist for smoked salmon
Paul and Karak (2022)	India	VECM, TVECM, TAR, and MTAR	Asymmetric price transmission and nonlinear co-movement were confirmed
Huffaker et al. (2021)	Papua New Guinea	Causal detection method	Prices are transmitted upstream from the world market to domestic exporters and processors, but not to retailers.
Worako et al. (2008)	Ethiopia	Cointegration and error correction model	Asymmetric price transmission (negative changes in world coffee prices transmit much faster than positive ones)
Varela (2012)	Bolivia	Engle-Granger cointegration approach	Prices are integrated with the world markets, with the incomplete and slow transmission. Domestic price responds faster when the world market price rises than when it falls.
Miller and Hayenga (2001)	US	Engle’s band spectrum regression	Asymmetric price transmission is confirmed but sensitive to the data frequency.
Ghoshray (2008)	Thailand and Vietnam	M-TAR model	Asymmetric price adjustment (price adjustment to the long-run equilibrium is relatively faster when the price differential is decreasing than when it decreases.)
Kumar (2017)	India	Nonlinear Granger causality and nonlinear ARDL	The relationship between oil and gold prices is nonlinear and asymmetric. Gold prices are more sensitive to positive shocks in oil prices.
Schweikert (2019)	US and Germany	Quantile autoregression and bootstrap cointegration	Price asymmetries are common in the early stages production chain, but are not fully transmitted to the retailers
Laili et al. (2020)	Indonesia	The asymmetric error correction model	Consumer prices are more responsive to producer price rises than falls both in the long- and short-run.
Mai et al. (2019)	Vietnam	TVECM	When the long-run divergence surpasses a specific threshold, farm gate prices respond faster to drops than rises in export prices.
Baffes and Gardner (2003)	8 countries	Error correction model	Domestic commodity prices are integrated with world markets only in Chile, Mexico, and Argentina
Dong et al. (2018)	China	TAR/AECM/ARMA	Symmetric transmission between pork and pig prices was observed when prices are lower and more stable and it turns to be asymmetric when prices are higher and volatile.
Rahmanta and Ayu (2020)	North Sumatra	AECM	Asymmetric price transmission occurred only in the short-run
Varela and Taniguchi (2014)	Indonesia	ECM	Asymmetric price transmission was confirmed (when deviated from the long-run equilibrium, the upward adjustment in the domestic wheat price is faster than the downward adjustment)
Mofya-mukuka and Abdulai (2013)	Tanzania and Zambia	Momentum threshold cointegration and ECM	Asymmetric price transmission was confirmed but the response of the domestic price to the world market price depends on the degree of market liberalization
Rezitis and Tsionas (2019)	Europe	Panel VECM	Positive price asymmetric transmission occurred in the European food market
Abunyuwah (2020)	Ghana	M-TAR	Ghanian yam price is cointegrated with the world market price and positive price transmission asymmetries are observed
Mclaren (2015)	117 countries	2SLS	There is more price transmission when prices fall than when they rise.
Nikiema and Sakurai (2020)	Burkina Faso	TVECM	Positive shocks cause prices to respond faster than negative shocks.

Figure 1. Relationship between domestic and international prices.

Source: Adopted from Kalkuhl (2016)

Table 2. Variables description and descriptive statistics

Variable	Notation	Source	Obs	Mean	Std.Dev.	Min	Max
ECX coffee price	Ep	ECX	144	1.319040	0.277071	0.920167	2.487482
World coffee price	Wp	ICO	144	1.335626	0.313074	0.933300	2.312400
Exchange Rate	ER	NBE	144	21.72917	7.104663	9.630000	38.57000
The volume of coffee Export	Vx	NBE	144	16,729.56	6542.750	3709.060	30,445.60
Inflation Rate	IR	NBE	144	15.21294	9.961029	2.422080	46.14267
Money Supply	M2	NBE	144	6299.553	2084.742	3243.680	10,856.99
Trade Openness	TOp	NBE	144	23.19343	6.379335	11.09184	42.13892
Growth Domestic Product	GDP	NBE	144	0.00004	0.000014	0.0000266	0.000071

Table 3. Kapetanios & Shin unit root test

Variables	KS Stat.	Variables	KS Stat.
Levels		First Differences
lnECXp	−2.342 (0)	lnEFP	−4.557 (1)***
lnWp	−1.769 (0)	lnY	−5.449 (2)***
lnER	0.864 (1)	lnER	−9.473 (0)***
lnVX	−3.297 (4)**	lnVX	−3.864 (1)***
lnIR	−2.997 (0)***	lnIR	−6.732 (1)***
lnM2	0.545 (0)	lnM2	−4.546 (1)***
lnTop	−0.491 (2)	lnTop	−2.253 (3)*
lnGDP	1.184 (0)	lnGDP	−10.877 (0)***

Note: KS stat. stands for Kapetanios & Shin statistic *** and ** refer to the rejection of the null hypothesis at 1% and 5% levels of significance respectively. The figures in parentheses are lag lengths.

Table 4. ARDL bounds Cointegration, and Bayer and Hanck Cointegration Tests

Model Specification	Selected Model	F-Stat.	Result
$\begin{array}{c}{F}_{lnEP}(lnEp|lnWp,lnER,lnVX,lnIR,\\ \hspace{0.17em}lnM,lnTO,lnGDP)\end{array}$	(2, 1, 0, 4, 1, 0, 3, 4)	7.00***	Cointegration
Bounds Test Critical Values	CV	I(0)	I(1)
	1%	2.54	3.91
	5%	1.97	3.18

Note: *** and ** refer to the rejection of the null hypothesis at 1% and 5% levels of significance.

Table 5. Long-run coefficients

Levels Equation
Case 1: No Constant and No Trend
Variable	Coefficient	Std. Error	t-Statistic	Prob.
lnER	−0.245994	0.109859	−2.239177	0.0270
lnGDP	−0.072911	0.052304	−1.393980	0.1659
lnIR	0.068471	0.020908	3.274834	0.0014
lnM2	0.139532	0.123468	1.130112	0.2607
lnTOP	−0.145049	0.095494	−1.518935	0.1315
lnVX	0.172553	0.073304	2.353931	0.0202
lnWP	0.736662	0.085328	8.633283	0.0000

Table 6. Error correction representation for the selected ARDL model

Variable	Coefficient	Std. Error	t-Statistic	Prob.
$\mathrm{\Delta }lnE{P}_{t-1}$	0.109789	0.041765	2.628738	0.0097
$\mathrm{\Delta }lnGDP$	0.617379	0.243794	2.532383	0.0126
$\mathrm{\Delta }lnGD{P}_{t-1}$	0.007560	0.240309	0.031460	0.9750
$\mathrm{\Delta }lnGD{P}_{t-2}$	0.493686	0.241012	2.048384	0.0427
$\mathrm{\Delta }lnGD{P}_{t-3}$	−0.857409	0.227792	−3.764001	0.0003
$\mathrm{\Delta }lnIR$	0.124632	0.013039	9.558053	0.0000
$\mathrm{\Delta }lnTOP$	0.038086	0.039996	0.952251	0.3429
$\mathrm{\Delta }lnTO{P}_{t-1}$	0.081091	0.041307	1.963145	0.0520
$\mathrm{\Delta }lnTO{P}_{t-2}$	0.170475	0.037586	4.535580	0.0000
$\mathrm{\Delta }lnVX$	0.022658	0.022228	1.019310	0.3101
$\mathrm{\Delta }lnV{X}_{t-1}$	−0.097426	0.022820	−4.269267	0.0000
$\mathrm{\Delta }lnV{X}_{t-2}$	−0.028747	0.022387	−1.284058	0.2016
$\mathrm{\Delta }lnV{X}_{t-3}$	−0.113907	0.021584	−5.277429	0.0000
$\mathrm{\Delta }lnWP$	0.780966	0.045488	17.16880	0.0000
$EC{T}_{t-1}$	−0.433611	0.056265	−7.706538	0.000

Table 7. NARDL Co-integration Test

	Long-run effect				[+]	Long-run effect				[-]
Exog. var.	coef.		F-stat		P > F	coef.		F-stat		P > F
lnWp	1.955		16.450		0.000	−0.596		5.395		0.025
lnER	−0.183		0.026		0.873	10.391		1.693		0.200
lnVX	−0.215		1.451		0.235	−0.346		1.254		0.269
lnIR	−0.005		0.002		0.962	0.047		0.208		0.650
lnM2	0.459		0.343		0.561	−1.343		0.953		0.334
lnTop	−0.924		3.017		0.090	1.232		4.836		0.033
lnGDP	−0.540		0.034		0.855	0.000		.		.
		Long-run asymmetry					Short-run asymmetry
		F-stat		P > F			F-stat		P > F
lnWp		5.987		0.019			3.922		0.054
lnER		1.654		0.205			1.798		0.187
lnVX		2.940		0.094			1.921		0.173
lnIR		0.333		0.567			0.986		0.326
lnM2		0.460		0.501			2.431		0.126
lnTop		1.789		0.188			1.947		0.170
lnGDP		0.034		0.855			1.683		0.201

Note: Long-run effect [-] refers to a permanent change in an exogenous variable by −1.

Figure 2. Plot of the cumulative sum of recursive residuals.

Figure 3. Plot of the cumulative sum of squares of recursive residuals.

Table

Model diagnostics	Stat.	p-value
Portmanteau test up to lag 40 (chi2)	52.25	0.0928
Breusch/Pagan heteroskedasticity test (chi2)	.6081	0.4355
Ramsey RESET test (F)	2.648	0.0620
Jarque-Bera test on normality (chi2)	2.144	0.3423

Table

Variable	Coefficient	Std. Error	t-Statistic	Prob.
$\mathrm{\Delta }$ lnWP^+ (−1)	0.868811	0.099034	8.772832	0.0000
$\mathrm{\Delta }$ lnWP^−(−1)	0.713032	0.112712	6.326123	0.0000

Table 8. VAR lag order selection criteria

Lag	LogL	LR	FPE	AIC	SC	HQ
0	438.7112	NA	2.45e-13	−6.333988	−6.162655	−6.264362
1	1347.614	1697.510	9.86e-19	−18.75904	−17.21704*	−18.13241*
2	1405.418	101.1567	1.09e-18	−18.66792	−15.75526	−17.48429
3	1478.898	119.9455	9.70e-19	−18.80733	−14.52401	−17.06670
4	1555.203	115.5794	8.47e-19	−18.98828	−13.33431	−16.69065
5	1613.934	82.04987	9.87e-19	−18.91079	−11.88615	−16.05615
6	1694.455	103.0200	8.70e-19	−19.15375	−10.75845	−15.74211
7	1849.953	180.6526*	2.68e-19*	−20.49932	−10.73336	−16.53068
8	1929.318	82.86557	2.71e-19	−20.72526*	−9.588638	−16.19962

Table 9. Toda-Yamamoto Granger causality test result

Dependent variables	Sources of Causation
	lnECXp	lnWp	lnER	lnVX	lnIR	lnM^2	lnTO	lnGDP
	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$	${\chi }^2(8)$
lnECXp	-	14.71*	6.3	31.47***	39.59***	18.81**	14.52*	32.48**
lnWp	13.10	-	20.30***	38.34***	60.20***	17.58**	23.70***	22.31***
lnER	20.17***	16.01**	-	15.80**	26.60***	15.01*	7.85	23.33***
lnVx	19.22**	19.25**	15.79**	-	26.15***	36.08***	26.89***	58.08***
lnIR	17.75**	12.91	6.81	16.27**	-	9.20	7.18	19.95**
LnM^2	9.47	20.64***	21.16***	22.12***	21.61***	-	4.81	20.95***
lnTOP	26.77***	16.94***	16.44**	32.62***	19.34**	20.83***	-	41.28***
lnGDP	80.62***	75.63***	39.16***	74.50***	25.57***	51.11***	24.33***	-

Note: ***, **, and * refer to the rejection of the null hypothesis at 1%, 5%, and 10% levels of significance.

Table 10. Frequency Domain Causality Test

Direction of causality	Long-term	Medium-term	Short-term
	${\omega }_i=0.05$	${\omega }_i=1.5$	${\omega }_i=2.5$
lnWp $\Rightarrow$ lnECXp	10.3697***	0.9363	4.1669
lnER $\Rightarrow$ lnECXp	4.2699	1.0330	3.9745
lnVX $\Rightarrow$ lnECXp	0.5009	2.7392	9.8427***
lnIR $\Rightarrow$ lnECXp	5.2460*	0.4296	0.5819
lnM2 $\Rightarrow$ lnECXp	10.7308***	2.1958	5.8766*
lnTop $\Rightarrow$ lnECXp	5.3561 *	0.2525	7.9045**
lnGDP $\Rightarrow$ lnECXp	8.3099 **	4.3179	5.2306*

Note: ***,** and * indicates rejection of the null hypothesis at 1%, 5% and 10% respectively.

Figure 4. Effect of counterfactual shock in world coffee prices on ECX coffee prices.

Figure 5. Effect of counterfactual shock in the exchange rate on ECX coffee prices.

Figure 6. Effect of counterfactual shock in a volume of export on ECX coffee prices.

Figure 7. Effect of counterfactual shock in the interest rate on ECX coffee prices. (a) Negative shock. (b) Positive shock.

Figure 8. Effect of counterfactual shock in money supply on ECX coffee prices.

Figure 9. Effect of counterfactual shock in trade openness on ECX coffee prices.

Figure 10. Effect of counterfactual shock in GPD on ECX coffee prices.

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