Investigating Fisher effect in SACU countries: A wavelet coherence approach

Figures & data

Table 1. Literature summary for SACU countries

Author name	Country	Time-period	Methods	Findings
Wesso (2000)	South Africa	1985–1999	VECM	Little evidence of traditional Fisher effect
Mitchell-Innes et al. (2007)	South Africa	2000–2005	VECM	Weak cointegrating long-run relationship
Phiri and Lusanga (2011)	South Africa	1980–2011	TAR and TVEC	Significant long-run Fisher effects
Peyavali and Sheefeni (2013)	Namibia	1992:01–2011:12	VECM	No Fisher, absence of cointegration effects
Khumalo et al. (2017)	Eswatini	2010–2014	OLS	Significant Fisher effect
Bosupeng (2015)	Botswana, Lesotho, SA and Namibia	2005–2013	VECM	Fisher effect exists for Lesotho and Botswana
Yaya (2015)	Benin, Cameroon, Cote d’Ivoire, Gabon, Gambia, Ghana, Kenya, Nigeria, Senegal, and South Africa.	1970–2013	ARDL	Over-ruling evidence indicate no Fisher Effect found.
Bahmani-Oskooee et al. (2016)	BRICS	1996:m01–2015:m09	Threshold ARDL	Fisher effect found, except for South Africa.
Nemushungwa (2016)	South Africa	2001:q1–2014:q4	ARDL	Partial Fisher effect.
Bayat et al. (2018)	Indonesia, India, Brazil, Turkey and South Africa	2000:m01–2016:m06	Conventional Panel cointegration and causality tests.	Fisher Effect found only in Brazil and Indonesia.
Phiri and Mbekeni (2020)	South Africa	2002:q1–2020:q2	Nonlinear ARDL	Stronger Fisher effect during rising periods in pre-crisis whilst stronger Fisher effect during falling period in post-crisis
Phiri (2021)	South Africa	2002:q3–2019:q4	Frequency domain causality tests	NeoFisher effect found

Source: Author’s Own Tabulation.

Table 2. Descriptive statistics

Interest_rates	Mean	Maximum	Minimum	Std.Dev	Observations
Botswana	9,63	15,50	4,75	3,74	180
Eswatini	7,00	11,50	5,00	1,82	180
Lesotho	7,33	13,24	4,94	1,96	1806
Namibia	7,11	10,50	5,50	1,44	180
South Africa	11,18	21,85	4,69	4,56	180
Inflation_rates	Mean	Maximum	Minimum	Std.Dev	Observations
Botswana	6,43	15,06	2,10	3,31	180
Eswatini	6,39	14,71	1,60	2,57	180
Lesotho	7,32	35,14	2,00	6,75	180
Namibia	5,65	12,25	0,94	2,17	180
South Africa	8,93	20,70	0,20	4,60	180

Source: Author’s Own Tabulation.

Table 3. Correlation estimate between interest rates and inflation

Country	Correlation
Botswana	0,8622***
Eswatini	0,5602**
Namibia Lesotho	0,4445** 0,6180**
South Africa	0,5247**

Source: Author’s Own Tabulation. “***” denotes 1% significance level.

Table 4. Preliminary estimates between interest rates and inflation

	Botswana	Eswatini	Namibia	Lesotho	South Africa
Estimator
OLS	0.94 (0.00)***	0.46 (0.00)***	0.41 (0.00)***	0.37 (0.00)***	0.77 (0.00)***
FMOLS	1.01 (0.00)***	0.49 (0.00)***	0.45 (0.00)***	0.41 (0.00)***	0.82 (0.00)***
DOLS	1.01 (0.00)***	0.49 (0.00)***	0.44 (0.00)***	0.41 (0.00)	0.83 (0.00)***
ARDL	1.34 (0.00)***	0.53 (0.14)	0.21 (0.77)	0.40 (0.15)	0.36 (0.58)

Notes: p-values reported in (). “***” denotes 1% significance level.

Figure 1. Time series and WPS plot for interest rates in South Africa.

Figure 2. Time series and WPS plot for interest rates in Botswana.

Figure 3. Time series and WPS plot for interest rates in Lesotho.

Figure 4. Time series and WPS plot for interest rates in Namibia.

Figure 5. Time series and WPS plot for interest rates in Eswatini.

Notes: From Figures 1 to 5 the time series is shown on the left and the WPS on the right. From the WPS the white contour line represents the significance level. The strength of variation within the series ranges from blue colour (weak correlation) to red colour (strong correlation).

Figure 6. Time series and WPS plot for inflation in South Africa.

Figure 7. Time series and WPS plot for inflation in Botswana.

Figure 8. Time series and WPS plot for inflation in Lesotho.

Figure 9. Time series and WPS plot for inflation in Namibia.

Figure 10. Time series and WPS plot for inflation in Eswatini.

Notes: From Figures 6 to 10 the time series is shown on the left and the WPS on the right. From the WPS the white contour line represents the significance level. The strength of variation within the series ranges from blue colour (weak correlation) to red colour (strong correlation).

Figure 11. Time series and wavelet coherence plot for inflation and interest rates in South Africa.

Figure 12. Time series and wavelet coherence plot for inflation and interest rates in Botswana.

Figure 13. Time series and wavelet coherence plot for inflation and interest rates in Lesotho.

Figure 14. Time series and wavelet coherence plot for inflation and interest rates in Namibia.

Figure 15. Time series and wavelet coherence plot for inflation and interest rates in Eswatini.

Notes: From Figures 12-16 the time series co-movement is shown on the left and the wavelet coherence plots on the right. From the wavelet plots the white contour line represents the significance level. The power of correlation ranges from blue colour (weak correlation) to red colour (strong correlation). The arrow notations, $\uparrow$, $\nearrow$, $\to$, $\searrow$, ($\downarrow$, $\swarrow$, $\leftarrow$, $\nwarrow$) indicate that a positive (negative) relationship between the series. The arrow notations,$\uparrow$, $\nearrow$, $\to$, $\swarrow$,$\leftarrow$, ($\searrow$,$\downarrow$,$\nwarrow$) indicate that nominal interest rates are leading (lagging) inflation rates

Figure 16. Diagram of phase dynamics.

Wesso, G. (2000, June). Long-term yield bonds and future inflation in South Africa: A vector error-correction analysis. Quarterly Bulletin. Pretoria: South African Reserve Bank. https://www.resbank.co.za/content/dam/sarb/publications/quarterly-bulletins/articles-and-notes/2000/4790/Article---Long-term-bond-yields-and-future-inflation-in-SA.pdf

 Google Scholar

Mitchell-Innes, H., Aziakpono, M., & Faure, A. (2007). Inflation targeting and the Fisher effect in South Africa: An empirical investigation. South African Journal of Economics, 75(4), 693–707. https://doi.org/10.1111/j.1813-6982.2007.00143.x

 Web of Science ®Google Scholar

Phiri, A., & Lusanga, P. (2011). Can asymmetries account for the empirical failure of the Fisher effect in South Africa? Economics Bulletin, 31(3), 1968–1979. http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I3-P178.pdf

 Google Scholar

Peyavali, J., & Sheefeni, S. (2013). Testing for the Fisher Hypothesis in Namibia. Journal of Emerging Issues in Economics, Finance and Banking, 2(1), 571–582. https://www.researchgate.net/publication/308202541_Testing_for_the_Fisher_Hypothesis_in_Namibia

 Google Scholar

Khumalo, C., Mutambara, E., & Akwesi Assensoh- Kodua, A. (2017). Relationship between inflation and interest rates in Swaziland revisited. Banks and Bank Systems, 12(4), 218–226. https://doi.org/10.21511/bbs.12(4-1).2017.10

 Google Scholar

Bosupeng, M. (2015). The fisher effect using differences in the deterministic term. International Journal of Latest Trends in Finance and Economic Sciences, 4(5), 1031–1040.

 Google Scholar

Yaya, K. (2015). Testing the long-run Fisher effect in selected African countries: Evidence from ARDL bounds test. International Journal of Economics and Finance, 7(12), 168. https://doi.org/10.5539/ijef.v7n12p168

 Google Scholar

Bahmani-Oskooee, M., Li, J., & Chang, T. (2016). Revisiting Fisher equation in BRICS countries. Journal of Global Economics, 4(3), 1–3. https://doi.org/10.4172/2375-4389.1000210

 Google Scholar

Nemushungwa, A. (2016), ‘An empirical study of Fisher effects and the dynamic relation between nominal interest rates and inflation in South Africa”, AP16 Mauritius Conference. Ebene-Mauritius: Global Bizresearch.

 Google Scholar

Bayat, T., Kayhan, S., & Tasar, I. (2018). Re-visiting fisher effect for fragile five economies. Journal of Central Banking Theory and Practice, 7(1), 203–218. https://doi.org/10.2478/jcbtp-2018-0019

 Google Scholar

Phiri, A. (2021). Is NeoFisherism ‘alive’ in South Africa? A frequency domain causality approach. Macroeconomics and Finance in Emerging Market Economies, 14(2), 142–156. https://doi.org/10.1080/17520843.2020.1796732

 Web of Science ®Google Scholar