The unit root tests used in this analysis can of course be seen in Table 2, the test results show the statistical significance of the parameters, of course, we used the test proposed in (Levin, Lin, & Chu, 2002).This is an asymptotic test based on Dickey-Fuller and augmented Dickey-Fuller.
Table 3
Panel Unit Roots Test
LLC Unit Roots
|
Variable
|
∆lnProd
|
∆lnO_P
|
∆lnD_P
|
∆lnG_P
|
∆lnreer
|
∆lnX
|
Statistic (P)
|
∆lnProd t-2
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
26.475.226
|
∆lnO_P t-2
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
12.085.878
|
∆lnD_P t-2
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
2.823.167
|
∆lnG_P t-2
|
0.0011
|
0.0011
|
0.0011
|
0.0011
|
0.0011
|
0.0011
|
1.585.907
|
∆lnreer t-2
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
13.313.855
|
∆lnX t-2
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
0.0000
|
9.128.347
|
Notes: The table does, however, present the panel unit root tests, naturally using the test proposed in (Levin, Lin, & Chu, 2002)... Source: Estimation results
The results of the panel unit root tests show that the presence of unit roots in the series analysed is relevant. On the other hand, two lags were used in the model at the outset, which allowed unit roots to be eliminated. The results therefore suggest that there are stationary series in the model.
Table 4
KAO Cointegration Test
|
|
Statistic
|
p-value
|
Modified Dickey-Fuller t
|
-32.165
|
0.0006
|
Dickey-Fuller t
|
-98.566
|
0.0000
|
Augmented Dickey-Fuller t
|
-0.6338
|
0.2631
|
Unadjusted modified Dickey Fuller t
|
-81.933
|
0.0000
|
Unadjusted Dickey-Fuller t
|
-118.149
|
0.0000
|
Notes: The table shows the cointegration test proposed in (Kao, 1999)naturally being DF and ADF based tests. Source: Estimation results. |
The relationships between the variables were in fact proven using the cointegration test proposed in (Kao, 1999). Thus, the results suggest that there are long-term equilibrium relationships between the variables in the model, and that the existence of variables in levels, for example, is naturally proven.
However, the tests proposed in (Granger, 1969) suggest that it is possible to predict the dependent variable using the information provided by the model's explanatory variables. Thus, the results in Table 3 do help to explain it. On the other hand, we used, for example, the criteria for optimum choice of the number of lags to include in the models, so according to the criteria we used significantly two lags in the model, according to the results in Table 5.
Table 5
Mismatches
|
p-value
|
MBIC
|
MAIC
|
MQIC
|
1
|
0.0000
|
45,67889
|
32.5678
|
5,5678
|
2*
|
0.0000
|
3,0987
|
78.098
|
6,2345
|
3
|
0.0000
|
98,34567
|
8,2345
|
23,89765
|
4
|
0.0000
|
567.890
|
6,87756
|
34,09834
|
Source: Estimation results |
Table 6
Granger Causality Test
∆Prod chi2 df Prob > chi2
|
∆lnO_P 2.876 2 0.237
|
∆lnD_P 1.232 2 0.540
|
∆lnG_P 4.544 2 0.103
|
∆lnreer 0.275 2 0.871
|
∆lnX 3.809 2 0.149
|
ALL 16.301 10 0.091
|
-
|
∆lnO_P
|
∆lnProd 2.765 2 0.251
|
∆lnD_P 2.983 2 0.225
|
∆lnG_P 141.810 2 0.000
|
∆lnreer 0.276 2 0.871
|
∆lnX 0.698 2 0.705
|
ALL 217.521 10 0.000
|
-
|
∆lnD_P
|
∆lnProd 0.715 2 0.699
|
∆lnO_P 27.037 2 0.000
|
∆lnG_P 9.114 2 0.010
|
∆lnreer 1.331 2 0.514
|
∆lnX 1.366 2 0.505
|
ALL 49.110 10 0.000
|
-
|
∆lnG_P
|
∆lnProd 0.714 2 0.700
|
∆lnO_P 28.258 2 0.000
|
∆lnD_P 11.067 2 0.004
|
∆lnreer 0.667 2 0.717
|
∆lnX 3.879 2 0.144
|
ALL 77.648 10 0.000
|
-
|
∆lnreer
|
∆lnProd 18.529 2 0.000
|
∆lnO_P 0.655 2 0.721
|
∆lnD_P 2.526 2 0.283
|
∆lnG_P 0.988 2 0.610
|
∆lnX 3.363 2 0.186
|
ALL 51.273 10 0.000
|
-
|
∆lnX
|
∆lnProd 1.967 2 0.374
|
∆lnO_P 2.501 2 0.286
|
∆lnD_P 0.785 2 0.675
|
∆lnG_P 3.814 2 0.149
|
∆lnreer 0.269 2 0.874
|
ALL 7.182 10 0.708
|
Source: Estimation results.
Table 7
Variance Decomposition Results
Forecast
Response Variable and Forecast horizont
|
Impulse variable
|
0 0
|
0 0
|
0
|
0
|
0
|
1 1
|
0 0
|
0
|
0
|
0
|
2 .8533901
|
.004181 .0250903
|
.0014676
|
.0041954
|
.1116757
|
3 .8569584
|
.0109299 .0199344
|
.0014849
|
.0037865
|
.1069057
|
4 .7768052
|
.0104876 .0186871
|
.0185465
|
.0034197
|
.172054
|
5 .7779735
|
.0097291 .0172907
|
.0322925
|
.0031765
|
.1595377
|
6 .6878269
|
.0118969 .0173296
|
.0689259
|
.002791
|
.2112296
|
7 .6707371
|
.012026 .01737
|
.0753423
|
.0025222
|
.2220023
|
8 .6211758
|
.0106363 .0184117
|
.0693867
|
.0020615
|
.2783278
|
9 .613048
|
.0090489 .0183149
|
.0579254
|
.0019286
|
.2997344
|
10 .6109399
|
.0083122 .0176246
|
.0469005
|
.0019201
|
.3143028
|
D2DlnO_P
|
|
|
|
|
0 0
|
0 0
|
0
|
0
|
0
|
1 .0016604
|
.9983397 0
|
0
|
0
|
0
|
2 .0180627
|
.9323926 .0059527
|
.0263704
|
.0035845
|
.0136371
|
3 .0128306
|
.5627446 .0142328
|
.3888144
|
.0024428
|
.0189347
|
4 .0109527
|
.4912934 .0123462
|
.4086787
|
.0024627
|
.0742663
|
5 .0533854
|
.4279484 .0298986
|
.3269799
|
.0024061
|
.1593816
|
6 .1123734
|
.3307295 .026287
|
.2525013
|
.0022177
|
.2758911
|
7 .2247763
|
.2472114 .0229228
|
.2050662
|
.0018831
|
.29814
|
8 .3432362
|
.1938679 .0211343
|
.1585649
|
.0027299
|
.2804667
|
9 .4182739
|
.1687623 .0178953
|
.1303066
|
.0037565
|
.2610055
|
10 .4598797
|
.1410567 .0162711
|
.1312622
|
.0034574
|
.248073
|
D2DlnD_P
|
|
|
|
|
0 0
|
0 0
|
0
|
0
|
0
|
1 .000823
|
.0012633 .9979137
|
0
|
0
|
0
|
2 .015132
|
.0054173 .9318188
|
.0219745
|
.0030155
|
.022642
|
3 .0240784
|
.0830497 .7830151
|
.0510257
|
.0179179
|
.0409132
|
4 .0989894
|
.0779373 .7196282
|
.047769
|
.0178817
|
.0377945
|
5 .1047304
|
.0726274 .6668
|
.0920705
|
.0279022
|
.0358695
|
6 .1023952
|
.0702646 .6450793
|
.1183458
|
.0270209
|
.0368941
|
7 .1035685
|
.0837846 .5949633
|
.1390887
|
.0268893
|
.0517057
|
8 .1108747
|
.0792869 .5474601
|
.1402538
|
.0247585
|
.0973659
|
9 .1457485
|
.0702198 .486798
|
.1244205
|
.0219559
|
.1508572
|
10 .2129907
|
.0601575 .4165799
|
.1068836
|
.0190757
|
.1843126
|
D2DlnG_P
|
|
|
|
|
0 0
|
0 0
|
0
|
0
|
0
|
1 .0085277
|
.0525072 .0126526
|
.9263125
|
0
|
0
|
2 .0082432
|
.1040481 .0066424
|
.8441858
|
.0011896
|
.035691
|
3 .0177905
|
.1490456 .0122605
|
.6473981
|
.0010996
|
.1724057
|
4 .0798875
|
.113872 .0166347
|
.4567301
|
.0007603
|
.3321154
|
5 .1995308
|
.0719001 .0215157
|
.2947694
|
.0005606
|
.4117234
|
6 .3363671
|
.0551736 .0194298
|
.2050409
|
.0014996
|
.3824889
|
7 .4370256
|
.055133 .0162952
|
.1487339
|
.002774
|
.3400382
|
8 .5067623
|
.0498827 .0145283
|
.1212442
|
.0032294
|
.304353
|
9 .5270559
|
.040064 .0132845
|
.1325731
|
.0029457
|
.2840769
|
10 .5108835
|
.0318765 .0128575
|
.1587254
|
.002327
|
.2833301
|
D2Dlnreer
|
|
|
|
|
0 0
|
0 0
|
0
|
0
|
0
|
1 .0133214
|
.042796 .0079575
|
.0146105
|
.9213147
|
0
|
2 .1332496
|
.0335731 .0095131
|
.0271044
|
.7475749
|
.0489849
|
3 .1331702
|
.0312907 .0085144
|
.0234281
|
.6643015
|
.1392951
|
4 .2289209
|
.0267238 .0124097
|
.0201001
|
.5741452
|
.1377004
|
5 .2676959
|
.0237739 .0170859
|
.0251251
|
.4993272
|
.1669921
|
6 .3185997
|
.0219332 .0161868
|
.0289601
|
.4369086
|
.1774116
|
7 .3533928
|
.018276 .0154601
|
.03927
|
.3639359
|
.2096651
|
8 .3892869
|
.0149786 .0148584
|
.0495907
|
.2983913
|
.2328941
|
9 .4164112
|
.0118195 .0152156
|
.0559093
|
.2345698
|
.2660746
|
10 .4454965
|
.0091111 .0158566
|
.0563894
|
.1807783
|
.2923681
|
D2DlnX
|
|
|
|
|
0 0
|
0 0
|
0
|
0
|
0
|
1 .3814024
|
.0259685 .0012014
|
.0223561
|
.0005407
|
.5685309
|
2 .4168421
|
.0260164 .0087443
|
.0201348
|
.0055834
|
.5226791
|
3 .543613
|
.0188239 .0143803
|
.0115471
|
.0044396
|
.4071961
|
4 .565562
|
.0209457 .0144336
|
.0112989
|
.0044422
|
.3833176
|
5 .6027432
|
.0180636 .0124249
|
.0257859
|
.0034986
|
.3374839
|
6 .5813849
|
.0134261 .0127329
|
.0572157
|
.0028472
|
.3323931
|
7 .5620775
|
.0103671 .0129468
|
.0827638
|
.002201
|
.3296439
|
8 .5342051
|
.0081973 .0142271
|
.088967
|
.0016581
|
.3527455
|
9 .5298866
|
.0059672 .0153826
|
.0792682
|
.0013176
|
.3681778
|
10 .5350204
|
.0042254 .015946
|
.0653589
|
.001223
|
.3782264
|
Notes: The table shows the results of the Variance decomposition. Source: Estimation results. |
Table 8
Results of the Panel VAR Model
Variable
|
∆lnProd
|
∆lnO_P
|
∆lnD_P
|
∆lnG_P
|
∆lnreer
|
∆lnX
|
∆lnProd
|
(-0.373)
(-1.72)
|
(-0.602)
(-1.47)
|
(-0.948)
(-0.77)
|
0.102 (0.44)
|
(-0.149)
(-1.04)
|
0.293
(0.44)
|
∆lnProd
|
0.460
(0.93)
|
(-0.694)
(-0.91)
|
(-0.912)
(-0.47)
|
0.333 (0.71)
|
0.120 (0.41)
|
0.993
(0.90)
|
∆lnO_P
|
(-0.0662)
(-1.37)
|
(-0.475)***
(-4.51)
|
(-0.220)
(-1.17)
|
(-0.297)***
(-4.63)
|
0.0235 (0.49)
|
(-0.243)
(-1.57)
|
∆lnO_P
|
(-0.00852)
(-0.23)
|
(-0.431)***
(-5.24)
|
(-0.609)***
(-5.12)
|
(-0.244)***
(-5.10)
|
0.0261 (0.77)
|
(-0.0927)
(-0.94)
|
∆lnD_P
|
0.0390
(1.06)
|
(-0.0863)
(-1.52)
|
(-0.135)
(-1.23)
|
(-0.0205)
(-0.54)
|
0.0120 (0.53)
|
0.0443 (0.61)
|
∆lnD_P
|
0.00739 (0.53)
|
0.0103 (0.22)
|
(-0.308)**
(-2.77)
|
(-0.102)**
(-3.28)
|
0.0303 (1.53)
|
0.0339 (0.74)
|
∆lnG_P
|
0.0828 (1.04)
|
0.390* (2.03)
|
0.532*
(2.18)
|
0.931***
(12.80)
|
0.0443 (0.65)
|
0.336
(1.61)
|
∆lnG_P
|
(-0.114)
(-1.04)
|
1.527***
(7.44)
|
0.406
(1.39)
|
0.120 (1.01)
|
(-0.0679)
(-0.92)
|
(-0.241)
(-1.25)
|
∆lnreer
|
0.0739
(0.36)
|
0.197 (0.44)
|
(-0.273)
(-0.50)
|
0.0992 (0.49)
|
0.195
(1.31)
|
0.236
(0.35)
|
∆lnreer
|
(-0.0422)
(-0.32)
|
(-0.0674)
(-0.18)
|
(-0.664)
(-1.13)
|
0.0962 (0.57)
|
(-0.230) (-1.73)
|
(-0.0531)
(-0.11)
|
∆lnX
|
0.188
(1.10)
|
0.170 (0.66)
|
0.287
(0.87)
|
(-0.193)
(-1.21)
|
(-0.108) (-1.83)
|
0.607
(1.12)
|
∆lnX
|
(-0.106)
(-0.43)
|
0.0168 (0.07)
|
0.484
(1.17)
|
(-0.213)
(-1.93)
|
(-0.0506) (-0.70)
|
0.411
(0.70)
|
Notes: The table shows the results of the panel VAR model estimation. The * p < 0.05; ** p < 0.01 and *** p < 0.001 represent the significance levels for 5%; 10%; and 1%, respectively. Source: Estimation results |
Discussion of results
We used two lags in particular: on the one hand, it allowed us to obtain stationary series at the outset, and on the other, we had an excellent long-term equilibrium relationship between the series in the model.
However, the variance decomposition shows the quantification of exogenous shocks, which both affect production through prices and end up affecting the behaviour of exports from the outset, strongly supported by the significant decreases in production levels that the selected economies show from the outset. Some quantifiable endogenous shocks via political instability in some African countries, however, help contribute to the significant decreases in the economy's production levels in general. Plausibly suggesting economic non-resilience in a context of uncertainty.
In general terms, however, the results help us to understand how economies are not able to become resilient from the outset. This non-resilience is largely associated with some of the relevant variables used in the model, such as resilience through the prices of the main exports that the selected African countries trade on the international markets.
With the Impulse Functions answered, we can see that a large number of African countries have economies that are out of their control. This approach suggests that the authorities are unable to control their economies from the outset, but this is due to a number of reasons: the first has to do with the fact that these countries do not have an economy with diversified production capacity from the outset, and the second has to do with the fact that these countries have an economy that is focussed mainly on the adoptive expectations of international markets. Thus, it is assumed from the outset that there are sufficient reasons to show that there is, for example, a lack of effective control over their economies, which makes them on the one hand non-resilient.
The Impulse Response Functions show, however, that there are significant levels of economic resilience in some of the countries selected from the sample, which from the outset do not have significant dependence on international markets and are also able to control their economies from the outset, particularly in countries such as Ghana, Botswana, South Africa and Namibia.
The exogenous shocks of the international diamond markets do help to explain the economic resilience of the countries, which in fact have an economy based on expectations of the behaviour of the international diamond markets in general.
As an example, Botswana has an economy controlled by the exogenous shocks of the diamond and adaptive expectations markets. However, in the selected sample, Botswana proved to be significantly resilient in a context of uncertainty, especially in the presence of the relevant exogenous shocks.
Strong reasons could, however, be related to the levels of Botswana's resilience, so industrial capacity, for example, helps to explain the levels of resilience, on the other hand, the levels of competitiveness of the economies explain the levels of Botswana's resilience behaviour, which from the outset are strongly supported by the levels of infrastructure that the country presents from the outset.
However, the exogenous shocks of the international gold markets do help to explain how the economy behaves in a context of uncertainty, so the results suggest that there is, for example, non-dependence on both international markets and dependence on adaptive expectations.
Thus, it is assumed from the outset that the authorities are able to control the economy, especially in the context of exogenous shocks of uncertainty that have a significant influence from the outset. On the other hand, there are significant reasons that show from the outset, for example, the sustainability of economic resilience through the production capacity that the economies present from the outset, on the other hand, they have also been significantly supported by the levels of investment that these economies in particular present.
As a result of exogenous shocks in the international oil markets, the results suggest that there is a significant influence on the levels of adaptive expectations, so the oil-producing countries, such as Angola and Equatorial Guinea, are unable to control their economies from the outset, although the approach suggests that there is a control of the international markets over the petro-dependent economies.
In general terms, the relevance of both international markets and the behaviour of certain variables helps us to understand, for example, how the authorities should be able to control their economies from the outset, especially in a context of uncertainty, where exogenous shocks can significantly affect the ability of economies to become resilient.
Resilience, however, is achieved when countries are able to respond from the outset with productive capacity and significant levels of domestic production, quantifiable in significant increases in exports.
Generally speaking, countries that can control their economies from the outset will be able to control exports from the outset, avoiding the existence of economies based on international market behaviour and the non-existence of adaptive expectations economies from the outset.