Climate Warming: Is There Evidence in Africa?

Table 5

Estimates of in the deseasonalized monthly data using a long memory model with AR(1) disturbances.

	No regressors	An intercept	A linear time trend
(a) South Africa
DURBAN	0.252 (0.175, 0.351)	0.252 (0.175, 0.352)	0.237 (0.156, 0.343)
PRETORIA	0.195 (0.095, 0.319)	0.196 (0.095, 0.324)	0.201 (0.097, 0.347)
CAPE TOWN	0.208 (0.136, 0.299)	0.208 (0.136, 0.299)	0.207 (0.134, 0.299)
(b) Côte d’Ivoire
BONDOUKOU	0.383 (0.301, 0.486)	0.377 (0.295, 0.474)	0.361 (0.266, 0.473)
GAGNOA	0.254 (0.173, 0.356)	0.255 (0.174, 0.365)	0.181 (00612, 0.333)
ABIDJAN	0.259 (0.171, 0.370)	0.259 (0.171, 0.370)	0.259 (0.171, 0.370)
(c) Kenya
KISUMU	0.263 (0.143, 0.414)	0.268 (0.142, 0.436)	0.217 (0.011, 0.447)
MOMBASA	0.356 (0.298, 0.423)	0.356 (0.298, 0.423)	0.352 (0.292, 0.421)
NAIROBI	0.144 (0.084, 0.220)	0.145 (0.081, 0.221)	−0.019 (−0.107, 0.086)
	Intercept	Time trend	Seasonal AR
KISUMU	−0.12903 (−1.949)	0.00077 (1.932)	0.184
MOMBASA	−0.26021 (−1.915)	0.00165 (1.955)	0.623
NAIROBI	−0.46857 (−9.171)	0.00198 (10.468)	0.530

In bold: the significant estimates according to the deterministic terms. In parenthesis: the 95% confidence bands of the estimates of .