Fiscal sustainability in the presence of structural breaks: Does overconfidence on resource exports hurt government’s ability to finance debt? Evidence from Nigeria

Figures & data

Figure 1. Percentage of variation in public revenues or expenditures as % of GDP, 1961–2014.

Table 1. Descriptive statistics of the variables for the period 1981–2011

	Mean	Median	Min.	Max.	Std. Deviation
Rt	.0037	.0001	−.0001	.0235	.0062
Gt	.0018	.0001	−.0001	.0102	.0028

Note: Figures are in billion naira.

Table 2. Results of the ADF, PP, and KPSS unit root tests

Variables	ADF	PP	KPSS
Rt	−2.290	−2.175	.225^***
ΔRt	−7.699^***	−7.912^***	.252
Gt	−2.812	−2.812	.214^***
ΔGt	−8.259^***	−8.874^***	.205
BDt	−4.109^**	−4.109^**	.224^***
ΔBDt	−7.850^***	−19.226^***	. 067

Notes: All variables are in log. ADF and PP are the augmented Dickey–Fuller (1979) test and Phillips–Perron (1988) test, respectively. KPSS is Kwiatkowski–Phillips–Schmidt–Shin (1992) test. Lag length for ADF was chosen by Schwarz information criterion (SIC). Bandwidth for PP and KPSS using Bartlett Kernel.

^** Statistical significance levels for the rejection of the null hypothesis at the 5%.

^*** Statistical significance levels for the rejection of the null hypothesis at the 1%.

Table 3. Johansen cointegration test on revenues and spending without break

Number of cointegrating equations	Null	Statistic	Critical value
Trace statistic (λtrace)	r = 0	13.383	15.495
	r ≤ 1	.0008	3.845
Maximum eigenvalue statistic (λmax)	r = 0	13.382	14.265
	r ≤ 1	.0008	3.845

Notes: Lag length for Johansen cointegration tests are chosen based on Schwarz Information criterion (SIC).

Table 4. Zivot and Andrews test for unit roots with one structural break

	Rt	ΔRt	Gt	ΔGt	BDt	ΔBDt
TB	1996	1996	1996	1996	2000	2000
α	−.5922 (−4.110)	−1.514^*** (−7.861)	−1.291 (−4.010)	−4.239^*** (−6.550)	−.8640^*** (−7.045)	−2.357^*** (−7.603)
θ	.1470^* (4.958)	.0826 (3.207)	.2451^*** (6.231)	.1508 (4.790)	.0585^* (4.836)	.0454 (3.119)
γ	.0040 (1.321)	−.0042 (−2.099)	.0075 (1.453)	−.0087 (−3.667)	−.0004 (−.3864)	−.0033 (−2.377)
K	2	1	7	6	0	0
Critical values
1%	−5.57	−5.57	−5.57	−5.57	−5.57	−5.57
5%	−5.08	−5.08	−5.08	−5.08	−5.08	−5.08
10%	−4.82	−4.82	−4.82	−4.82	−4.82	−4.82

Note: Lag length for the test was chosen by Schwarz information criterion (SIC). Figures in brackets are t-statistics.

^* Statistical significance at the 10% level.

^** Statistical significance at the 5% level.

^*** Statistical significance at the 1% level.

Figure 2. Trends of public revenues and spending (in billion naira), 1961–2014.

Figure 3. Logarithmic of public revenues and spending, 1961–2014.

Table 5. Lumsdaine and Papell test for unit roots with two breaks

	Rt	ΔRt	Gt	ΔGt	BDt	ΔBDt
TB1	1968	1996	1968	1996	1971	2000
TB2	1997	2001	1997	2000	2000	2007
α	−.9189 (−5.902)	−1.622^*** (−9.302)	−1.325 (−5.838)	−2.271^** (−6.884)	−1.021^* (−6.658)	−2.516^*** (−8.623)
θ	−.0461 (−1.997)	.1205 (3.043)	−.0562 (−1.672)	.0777 (1.532)	−.0167^* (−1.753)	.0581 (3.343)
ω	.1923 (5.370)	−.0938 (−2.648)	.2369 (6.191)	−.1644 (−3.779)	.0571 (4.633)	−.0479 (−2.259)
γ	.0059 (5.019)	−.0051 (−.4460)	.0097 (5.651)	.0229 (1.148)	−.0012 (−2.919)	−.0057 (−1.505)
ψ	.0060 (2.309)	.0062 (.5250)	.0072 (2.093)	−.0226 (−1.1078)	−.0001 (−.0958)	.0112 (2.317)
K	1	1	4	3	1	2
Critical values
1%	−7.34	−7.34	−7.34	−7.34	−7.34	−7.34
5%	−6.82	−6.82	−6.82	−6.82	−6.82	−6.82
10%	−6.49	−6.49	−6.49	−6.49	−6.49	−6.49

Note: Lag length for the test was chosen by Schwarz information criterion (SIC). Figures in brackets are t-statistics.

^* Statistical significance at the 10% level.

^** Statistical significance at the 5% level.

^*** Statistical significance at the 1% level.

Table 6. Bai and Perron breakpoints test

h = 15	Test statistics	Critical value^**
	Sup FT(1) 23.037^**	11.47
	Sup F(2/1) 33.380^**	12.95
	Sup FT(2) 50.760^**	9.75
	UDmax101.519^**	11.70
	WDmax119.428^**	12.81
Coefficient estimates with two breaks
	${\widehat{\mathit{\delta }}}_1$	${\widehat{\mathit{\delta }}}_2$
	.8599 (.0363)	.9016 (.0576)
	Number of breaks selected
Sequential	2
LWZ	2
SIC	2
Corresponding estimated break dates
	${\widehat{T}}_1$	${\widehat{T}}_2$
	1990 [1990–1999]	2000 [2000–2014]

Notes: Test statistics employ HAC covariances (Prewhitening with lags = 1, Quadratic-Spectral kernel, Andrews bandwidth). Estimation allows heterogeneous error distributions across breaks. Figures in brackets under the coefficient estimates are standard errors.

^** Bai–Perron (2003) critical values and statistical significance at the 5% level.

Table 7. Johansen cointegration test on revenues and spending with breaks in 1990 and 2000

Number of cointegrating equations	Null	Statistic	10% critical value	5% critical value	1% critical Value
Hl(r)	r = 0	54.258^***	42.999	46.355	53.098
	r ≤ 1	20.239	21.468	24.027	29.335
Hc(r)	r = 0	34.019	29.046	31.828	37.496
	r ≤ 1	20.239^**	13.918	15.970	20.312

Note: Lag length for the test was chosen by Schwarz information criterion (SIC).

^** Statistical significance at the 5% level.

^*** Statistical significance at the 1% level.

Table 8. ADRL bounds test results for cointegration relationship

Critical value of the F-statistic for the bounds test results with intercept and no time trend
k	1%		5%		10%
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
3	5.333	7.063	3.710	5.018	3.008	4.150
Computed F-statistics
FR(R/G)	5.538

Note: Critical values are from Narayan (2005).

Table 9. The long run coefficients

ARDL		DOLS
Panel A: Long run elasticities
Constant	.0429^*** (.0414)	Constant	.0661^*** (.0038)
Gt	.7934^*** (.0485)	Gt	.8015^*** (.0437)
D1t	.0222* (.0127)	D1t	.0184 (.0118)
D2t	.0888^*** (.0165)	D2t	.0889^*** (.0155)
Panel B: Hansen stability tests	Test statistic	p Values
LC	.0643	>.2

Note: The lags are chosen based on Schwarz information criterion (SIC). Figures in brackets are standard errors.

^*** Rejection of hypothesis at the 1% significance level.

Table 10. Error correction models and Granger causality tests

Parameters	Dependent variable: ΔRt	Parameters	Dependent variable: ΔGt
Panel A: Short run elasticities
ϖ11	–	ϖ21	1.090^*** (.0560)
${\mathit{\lambda }}_{11}$	.8053^*** (.0414)	${\mathit{\lambda }}_{21}$	–
θ11	.0300^* (.0163)	θ31	−.0122(.0194)
θ21	.1009^***(.0153)	θ41	−.1137^***(.0178)
η1	−.6496^***(.1356)	η2	−.6360^***(.1266)
F4,48	74.911^***	F4,48	76.588^***
R^2	.90	R^2	.91
Panel B: Diagnostic tests
${\mathit{\chi }}_{Norm}^2(1)$	2.708 (.2582)	${\mathit{\chi }}_{Norm}^2(1)$	1.042 (.5941)
${\mathit{\chi }}_{LM}^2(1)$	2.060(.1512)	${\mathit{\chi }}_{LM}^2(1)$	.0655(.7979)
${\mathit{\chi }}_{WT}^2(1)$	22.958(.1922)	${\mathit{\chi }}_{WT}^2(1)$	23.431(.1746)

Notes: ${\mathit{\chi }}_{Norm}^2$ is the Jarque–Bera test for normality, ${\mathit{\chi }}_{LM}^2$ is the Serial Correlation LM Test, and ${\mathit{\chi }}_{WT}^2$ is the white hetroskedasticity test. The lags used to estimate the ARDL model are chosen based on Akaike Information criterion (AIC). Figures in brackets are standard errors.

^*** Rejection of hypothesis at the 1% significance level.

Table 11. Tests for symmetric cointegration

Parameters	Models
	TAR	MTAR
${\mathit{\rho }}_1^a$	−.8480 (.2239)	−.9734 (.2217)
${\mathit{\rho }}_2^a$	−.7045 (.1933)	−.6112 (.1934)
Threshold (τ)	.0082	.0107
No cointegration F (ρ1 = ρ2 = 0)^b	11.869^***	12.958^***
Symmetry F (ρ1 = ρ2)^c	.02812	1.757
k	1	1
Ljung–Box Q-statistics	.5290(.467)	1.869(.172)
JB	2.945(.2294)	4.350(.1136)

Note: “a” Entries in parentheses are the t-statistics for the null hypothesis of the ρ_i values. Entries of “b” represent the F-statistics that follow a nonstandard distribution of the sample values for the null hypothesis ρ₁ = ρ₂ = 0, and “c” Entries represent the sample F-statistics for the null hypothesis of symmetric adjustment ρ₁ = ρ₂. The appropriate lags for the TAR and M-TAR adjustment processes were chosen by AIC. Figures in brackets are standard errors. JB is the Jarque–Bera normality test which has an asymptotic χ²(2) distribution.

^*** Significance level at 1%.

Figure A1. Cointegration graph, 1961–2014.

Figure B1. Plots for revenues equation.

Figure B2. Plots for expenditures equation.

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22.10.1002/(ISSN)1099-1255

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Narayan, P. K. (2005). The government revenue and government expenditure nexus: Empirical evidence from nine Asian countries. Journal of Asian Economics, 15, 1203–1216.10.1016/j.asieco.2004.11.007

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