Chapter VI: Conclusions and Policy Recommendations
6.3 Limitation of the study and Areas for future research
This study identified and analyzed the factors influencing sesame exports in BFA using an advanced econometrical technique for the period of 47 years (1970-2016). The dimension of adjusted R square showed, statistically, that 49% of variation in export earnings are explained by other factors not included in this model. This was the constraint of data availability on some relevant factors such as the state’s budget allocated to investments in infrastructure (agricultural infrastructure and others), among others. This factor widely is seen in the literature as a key factor that can determine agricultural exports especially in developing countries. In addition, in a context of climate change, the variable of rainfall might have an impact on the sector performance. However, data on rainfall for BFA are not accurately available for the temporal length used in this study. Moreover, it is clear that farmers have switched to sesame production due the downward pressure on cotton prices, therefore including cotton price in the model could help to capture that effect. Nevertheless, as the rainfall data, producer price of cotton are not accurately available.
As some key factors affecting sesame exports have been identified, future studies can further investigate the trade flow of sesame between BFA and its major trade partners (Japan, Singapore, China...). In addition, further comparative studies between BFA performance and other African exporting countries could help to learn experience from these countries or policies that could enhance the country performance. These outcomes could help to prescribe appropriate trade policies and further strategies aiming to enhance exports and the country’s competiveness in the international markets.
47
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Exogenous variables: C D1 D2 D3 D4 Date: 03/19/19 Time: 22:25
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error
AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
A2. Zivot-Andrews Unit Root test with Structural break
Zivot-Andrews Unit Root Test Date: 03/25/19 Time: 12:03 Sample: 1970 2016
Included observations: 47
Null Hypothesis: LOGEXCHRATE has a unit root with a structural break in the intercept
Chosen lag length: 0 (maximum lags: 4) Chosen break point: 1994
t-Statistic Prob. *
Zivot-Andrews test statistic -3.695406 0.001015
1% critical value: -5.34
5% critical value: -4.93
10% critical value: -4.58
* Probability values are calculated from a standard t-distribution and do not take into account the breakpoint selection process
-4.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Zivot-Andrew Breakpoints
Zivot-Andrews Unit Root Test Date: 03/18/19 Time: 14:03 Sample: 1970 2016
Included observations: 47
Null Hypothesis: LOGXEARNINGS has a unit root with a structural break in the intercept
Chosen lag length: 1 (maximum lags: 4) Chosen break point: 1986
t-Statistic Prob. *
Zivot-Andrews test statistic -3.447123 0.000662
1% critical value: -5.34
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Zivot-Andrew Breakpoints
55 Zivot-Andrews Unit Root Test
Date: 03/18/19 Time: 14:03 Sample: 1970 2016
Included observations: 47
Null Hypothesis: LOGPROD has a unit root with a structural break in the intercept
Chosen lag length: 4 (maximum lags: 4) Chosen break point: 2008
t-Statistic Prob. *
Zivot-Andrews test statistic -1.339854 0.018617
1% critical value: -5.34
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Zivot-Andrew Breakpoints
Zivot-Andrews Unit Root Test Date: 03/18/19 Time: 14:03 Sample: 1970 2016
Included observations: 47
Null Hypothesis: LOGWEXPRICE has a unit root with a structural break in the intercept
Chosen lag length: 2 (maximum lags: 4) Chosen break point: 2007
t-Statistic Prob. *
Zivot-Andrews test statistic -3.947765 0.000719
1% critical value: -5.34
5% critical value: -4.93
10% critical value: -4.58
-4.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Zivot-Andrew Breakpoints
Warning: Critical values assume no exogenous series Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.666979 103.8335 83.93712 0.0009 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
57 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Warning: Rank Test critical values derived assuming no exogenous series Lags interval: 1 to 1
Selected (0.05 level*) Number of Cointegrating
Relations by Model
Data Trend: None None Linear Linear Quadratic
Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend
Trace 1 1 1 1 1
Max-Eig 1 1 1 1 1
*Critical values based on MacKinnon-Haug-Michelis (1999) A4. VECM output
[-10.1132]
D(LOGPROD(-1)) 0.014220 0.111595 -0.245692 -0.171136 -0.182560 0.062873 (0.15546) (0.02900) (0.15120) (0.09534) (0.05755) (0.04008) [ 0.09147] [ 3.84746] [-1.62500] [-1.79508] [-3.17223] [ 1.56881]
D(LOGPDPRICE(-1)) 0.583160 -0.030054 0.125450 0.017345 0.146921 0.067390 (0.31059) (0.05795) (0.30207) (0.19047) (0.11498) (0.08007)
59 Log likelihood -22.73408 52.81645 -21.48337 -0.731163 21.98342 38.26634 Akaike AIC 1.499293 -1.858509 1.443705 0.521385 -0.488152 -1.211838
A5. Granger causality test within the VECM
VEC Granger Causality/Block Exogeneity Wald Tests
D(LOGPDPRICE) 0.268971 1 0.6040
61
All 9.299992 5 0.0977
A6. Autocorrelation LM test output
VEC Residual Serial Correlation LM Tests Date: 03/21/19 Time: 15:44
Sample: 1970 2016 Included observations: 45
Null hypothesis: No serial correlation at lag h
Lag LRE* stat df Prob. Rao F-stat df Prob.
1 37.56640 36 0.3973 1.051731 (36, 103.8) 0.4096
Lag LRE* stat df Prob. Rao F-stat df Prob.
1 37.56640 36 0.3973 1.051731 (36, 103.8) 0.4096
*Edgeworth expansion corrected likelihood ratio statistic.
A7. White test for heteroskedasticity
VEC Residual Heteroskedasticity Tests (Levels and Squares) Date: 03/21/19 Time: 15:49
Sample: 1970 2016 Included observations: 45
Joint test:
Chi-sq df Prob.
344.3130 378 0.8924
A8. Test for normality: Skewness/Kurtosis and Jarque-Bera
0
2008 2009 2010 2011 2012 2013 2014 2015 2016 CUSUM 5% Significance
2008 2009 2010 2011 2012 2013 2014 2015 2016 CUSUM of Squares 5% Significance
A10. Wald test (null hypothesis rejected) Wald Test:
Equation: Untitled
Test Statistic Value df Probability
F-statistic 8.443265 (5, 34) 0.0000
Chi-square 42.21633 5 0.0000
Null Hypothesis: C(3)=C(4)=C(5)=C(6)=C(7)=0 Null Hypothesis Summary:
63
Normalized Restriction (= 0) Value Std. Err.
C(3) 2.309831 0.576254
C(4) 0.014220 0.155456
C(5) 0.583160 0.310587
C(6) 1.302969 0.528445
C(7) 2.115835 0.577834
Restrictions are linear in coefficients.
A11. Stability
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
A12. Endogenous graphs
12