




































69 

 

Finance, Accounting and Business Analysis 
Volume 7 Issue 1, 2025 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2025.1.06 

 

Asymmetric Impact of Interest Rate on Economic Growth in Kenya 
 

Talknice Saungweme1* , Glenda Maluleke2 , Nicholas M. Odhiambo3  
  
Department of Economics, University of South Africa, South Africa1 

Department of Economics, University of South Africa, South Africa2 

Department of Economics, University of South Africa, South Africa3 

* Corresponding author 

 

Info Articles   Abstract 
 

History Article: 

Submitted 30 January 2025 

Revised  17 April 2025 

Accepted 28 April 2025 

 Purpose: This study re-examines the relationship between interest rates 

and economic growth, focusing on the asymmetric effects of lending 

interest rates on Kenya's economic performance.  

Design/Methodology/Approach: The study applied the nonlinear 

autoregressive distributed lag (NARDL) model to ascertain the distinct 

impacts of positive and negative interest rate shocks on economic growth 

in both the short and long run. It uses yearly time series data spanning 

the years 1980-2021.  

Findings: The results of the cointegration tests found evidence 

supporting the existence of an asymmetric long-run relationship, while 

the Wald test results show that there is a long-run and short-run 

asymmetry link between interest rates and economic growth in Kenya. 

On average, positive changes in lending interest rates have no significant 

impact on economic growth in Kenya, both in the short and long run. 

However, negative interest rate shocks spur economic growth in the 

short run but impede growth in the long run.  

Research Limitations/Implication: The study is limited to the Kenyan 

context and the dataset range of 1980–2021. Future research could 

explore thresholds for optimal interest rate levels and include a broader 

range of countries for comparative analysis. 

Originality/Value: This study uniquely applies the NARDL framework 

to Kenya, providing new insights into the asymmetric impact of interest 

rates on economic growth.  

Paper Type:  Research Paper. 

 

Keywords:  

Economic growth, interest 

rates, Kenya, NARDL  
 

 

JEL: C32, E43, O42  

* Address Correspondence:   

E-mail: talknice2009@gmail.com1 

malulg@unisa.ac.za2 

odhianm@unisa.ac.za3 
 

 

 
  

http://faba.bg/
mailto:talknice2009@gmail.com1
mailto:odhianm@unisa.ac.za
https://orcid.org/0000-0003-1255-7837
https://orcid.org/0000-0002-5234-3115
https://orcid.org/0000-0003-4988-0259


T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

70 

 

INTRODUCTION 
 

Introduction, theoretical and empirical literature synthesis 
Beginning with the Asian Tiger economies and shifting to newly industrialised economies, such as 

China, South Africa, India, Russia and Brazil, the most powerful and successful strategy for poverty 

reduction is through sustained economic growth emanating from aggressive and rapid innovation and 

industrialisation (Chatterjee and Naka 2022; United Nations Industrial Development Organisation 2020). 

These two groups of countries pursued both export-oriented and import substitution strategies, while 

advancing innovative production technologies (Liu et al. 2023). While both monetary and nonmonetary 

factors can be drivers of economic growth, nonmonetary factors have received most of the attention in 

research studies. This insight serves as the motivation for this paper, which has two primary goals. The first 

is to thoroughly review and document the relationship between lending interest rates and economic growth 

in Kenya. Lending interest rates can have a significant impact on the rate and trajectory of economic growth 

by influencing the magnitude and return of investment, as well as the scope and composition of both saving 

and consumption. Infrastructure development, industrialisation, institutional investors, mutual funds, and 

the corporate sector are all exposed to risks stemming from interest rate volatility (Olasehinde-Williams et 

al. 2024). 

The second is to bridge the gap in interest rate modelling by applying a model that can be used to 

quantify and comprehend the nature of the link between interest rates and growth in Kenya. Specifically, 

this study investigates the asymmetric impact of interest rates on economic growth in Kenya using time 

series data from 1980 to 2021. The nonlinear ARDL method captures the positive and negative changes 

asymmetrically and the short- and long-run dynamics of interest rates on economic growth in Kenya, 

enabling a more precise analysis across different economic conditions (see Saungweme et al. 2024; Shin et 

al. 2014). To the best of our knowledge, this is the first analysis of its kind conducted in Kenya, and it is 

unique since it employs an advanced estimation procedure that takes into account the asymmetrical 

characteristics of lending interest rates. 

Therefore, the primary goals of this study are to complement previous growth research on Kenya and 

to support ongoing reforms in the areas of monetary, economic, and financial policy. These reforms are 

essential to preserving macroeconomic stability, preserving debt sustainability, strengthening market 

confidence, and enhancing the achievement of Kenya's medium-term growth prospects (International 

Monetary Fund/IMF 2024; Odhiambo and Saungweme 2023a; Saungweme and Odhiambo 2021). In 

addition, Kenya’s economy faces unique structural challenges and external shocks, such as fluctuating global 

interest rates and capital flows (IMF 2024). Therefore, understanding how interest rate fluctuations affect 

growth can provide insights into optimal policy decisions for sustainable development. 

From a theoretical standpoint, there are multiple opposing hypotheses about the relationship between 

interest rates and economic growth. The first is a cogent explanation of the boom-bust pattern offered by the 

Austrian school of economic thought. That is, low interest rates from the central bank would encourage 

investment bubbles, which would then lead to a burst in asset prices, a financial crisis, and a severe recession 

(Foldvary 2015). The rate of interest is interpreted by the Austrian school as reflecting a methodical 

discounting of future values. The Austrian hypothesis states that the relationship between interest rates and 

economic growth typically revolves around the time preference issue. For example, increased productivity 

could encourage people to invest more now, making present-day investing more preferred over future 

investment (Holmes 2011). Furthermore, the market for loanable funds—funds that are accessible for 

borrowing—determines the interest rate (Foldvary 2015). Borrowers will be able to access more funding for 

consumption and investment at reduced interest rates. In general, Austrian economics holds that a central 

bank's manipulation of money and interest rates is what causes recessions; the best way to prevent these 

controls is to let the money supply and interest rates be determined by free market forces in money and 

banking. 

Keynesian theory comes second. Keynes' approach to interest rate dynamics is in contradistinction 

with loanable funds theory. For Keynes, interest rate dynamics is based on his conception of ontological 

uncertainty, liquidity preference, investors’ expectations and animal spirits, financial institutions, financial 

markets, and institutional practices (Akram 2021). According to Keynes, the short-term interest rate is 

determined by the central bank's policy rate, which then affects the long-term interest rate (Akram 2021). 

These long-term interest rates then influence investment, saving and consumption decisions in the economy. 

The McKinnon-Shaw hypothesis comes in third. In their original individual works, they contended 

that financial policies in developing and emerging economies, including low and restricted interest rates and 

restrictive credit management, among other financial repression acts, result in a decrease in savings, 

investment and economic growth (Wilson and Odhiambo 2023). McKinnon (1973) studied an economy in 

which the vast majority of investors had very limited access to external financing. In his view, savers may 

find it more convenient to build up their money in financial assets until they have sufficient funds to invest 



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71 

 

in higher-yielding physical assets (Leshoro and Wabiga 2023). Thus, deposits can act as a channel for the 

accumulation of capital, rendering deposits and capital complementary assets. The availability of deposits 

with positive real rates of return may thus encourage both saving and capital accumulation. 

In contrast to McKinnon, Shaw (1973) focused more on external rather than internal financing 

options as a fundamental constraint to capital formation. Shaw also underlined the significance of positive 

real deposit rates as an incentive to save in financially depressed economies. Shaw (1973) emphasised that 

high deposit rates might encourage investment spending by enabling the credit supply to grow in accordance 

with the financing requirements of the economy's productive sectors (Iddrisu and Alagidede 2020). Thus, 

McKinnon (1973) and Shaw (1973) suggest that low interest rates do not really increase investment and 

economic growth (Owusu 2023). After analysing the McKinnon-Shaw arguments, Mohlo (1986) came to 

the conclusion that deposits and physical capital complement each other in an intertemporal fashion, with 

current deposits being used to fund future investments. This link suggests that higher deposit rates inhibit 

investment in the short run but will eventually boost it in the long run. 

There is currently little but growing empirical research on the link between interest rates and economic 

growth (Leshoro and Wabiga 2023; Adabor 2022). First, Leshoro and Wabiga (2023) looked at how both 

positive and negative interest rate shocks affect private investment in South Africa. The study employed 

annual time series data from 1971 to 2019 and a nonlinear autoregressive distributed lag technique. The 

results indicate that interest rates and private investment exhibit short-run and long-run asymmetric 

relationships, with private investment responding differently to negative and positive shocks in interest rates.  

Second, by applying the NARDL approach, Adabor (2022) tested the asymmetric impact of lending 

interest rates on economic growth in Ghana using yearly time series data covering the period of 1970 to 

2019. The study found evidence of long-run and short-run asymmetrical effects of lending on economic 

growth in Ghana. The findings further show that positive changes in lending rates generate a decrease of 

nearly 0.2% in economic growth while negative changes lead to an increase of about 0.7% in economic 

growth.  

Other non-asymmetric studies conducted on the interest rate-growth linkage include Lee and Werner 

(2023), Shaukat et al. (2019), Awad and Al Karaki (2019). Lee and Werner (2023) analysed the impact of 

interest rates on economic growth in 19 industrialised and emerging economies. The analysis used a time-

varying dynamic conditional correlation in a GARCH model and further tested the direction of causation 

between the two variables in the studied economies. The results provide evidence consistent with the 

conclusion that lowering interest rates is counterproductive when trying to stimulate the economy. 

Awad and Al Karaki (2019) examined the impact of bank lending on economic growth in Palestine 

using quarterly time series data for the period from 1996 to 2015. The study employed the vector 

autoregressive model and vector error correction model, as well as the Granger causality test to test the 

underlying relationships. The study found that there was a statistically insignificant relationship between 

bank lending and economic growth. Additionally, there is evidence of unidirectional causality that runs from 

economic growth to bank lending.  

Shaukat et al. (2019) studied the mechanism by which the real interest rate establishes a negative 

effect on economic growth in 38 transitory economies. The study applied a dynamic panel data technique 

based on the Generalised Method of Moments for the period 1996-2015. The study found that during the 

transition period of developing economies, a high real interest rate restricts the economy's potential to grow. 

Considering the aforementioned theoretical stances and empirical evidence, the goal of this study is 

to empirically test the interest rate-growth relationship tailored to the Kenyan context. The remainder of the 

research is arranged as follows: Section 2 provides an overview of interest rates and economic growth trends 

in Kenya. Data, methodology, and estimation techniques are provided in Section 3. The empirical analysis 

is presented in Section 4, and the main conclusions and policy implications are summed up in Section 5. 

 

 

Overview of interest rate and economic growth trends in Kenya 

Kenya experienced a general balance of payments surplus from 1964 to 1972, with the exception of 

1964, 1967, and 1971 (World Bank 2022). Interest rate policy in Kenya was largely dormant throughout this 

time. The government managed interest rates by setting minimum savings for all deposit-taking 

institutions and minimum lending rates for commercial banks, non-bank financial institutions, and building 

societies (Baynham 1989). A variety of internal and external causes, notably inflation brought on by a 

significant rise in oil prices and the consequences of a severe national drought in 1973, had a negative impact 

on the economy between 1972 and 1981 (IMF 1985). Following the shocks, the government re-examined its 

regulatory structure, which resulted in the progressive escalation of restrictions on imports, exports, interest 

rates, and domestic pricing. 

Between 1974 and 1989, the monetary authorities in Kenya used an administered interest rate 

framework. The employment of statutory credit ceilings and minimum savings deposit rates was a crucial 



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

72 

 

tool for managing market liquidity (IMF 1991a). A revision to the credit ceiling policy between 1989 and 

1991 resulted in the maximum rate being split between short-term and long-term lending (IMF 1991a). The 

major challenge with controlling interest rates was a widening gap between administered and effective bank 

lending rates. For this reason, the monetary authorities have been unable to sell enough quantities of 

government securities to limit the growth of the money supply (IMF 1991b). 

The government then started a series of economic adjustment programs with the World Bank and 

IMF in 1991 and continued them until 1996. The overarching policy framework placed a strong emphasis 

on trade liberalisation, interest rate deregulation, and macroeconomic stabilisation (Obrien and Ryan 1999). 

In 1991, this framework resulted in a comprehensive liberalisation policy ideology. This philosophy is 

supported by the theoretical expectations of the McKinnon-Shaw hypothesis, which postulates that easing 

regulatory restrictions on interest rates will increase the volume of funds in deposit-taking 

institutions (McKinnon 1973; Shaw 1973). 

Regarding economic growth, the years 1963 and 1973 can be regarded as a decade of exceptional 

growth, with an average annual real growth rate of 6.7% (World Bank 2022). The favourable weather and 

trade conditions for Kenya's commodity exports, along with the successful execution of the import-

substitution program, all contributed to the country's impressive economic performance (Odhiambo and 

Saungweme 2023b). Between 1974 and 1979, the country underwent an economic recession marked by 

declining terms of trade and rising oil prices (IMF 2022). The period 1980-85 can be described as an era of 

macroeconomic imbalance and stabilisation, with low growth. Economic and monetary policy interventions 

between 1985 and 1989, which were further enhanced by the 1986 coffee boom, led to an economic rebound. 

For the first half of this period, real GDP increased by an average of 5.3% yearly (World Bank 2022). Figure 

1 shows interest rates and GDP per capita growth rates for the period 1980 to 2022. 

 

 
Source: Authors’ compilation using World Bank (2022) data 

Figure 1. Interest rate and economic growth trends in Kenya (1980-2022) 

 

Figure 1 largely describes a negative correlation between interest rates and annual growth of GDP 

per capita in Kenya. Interest rates rose sharply during times of economic instability, such as 1980–1985, 

1989–1992, 1995–1996, 2008, and 2019. Despite the series of economic reforms and monetary reforms, 

interest rates in Kenya remained high over the period under review, 1980-2022. These high interest rates on 

loans from the banking sector have been perceived by some policymakers as an obstacle to greater 

investment, financial inclusion, and economic growth (IMF 2019). As a result, the monetary authorities in 

Kenya were compelled to revert to managing interest rates in 2016. As a result, the observable flip in interest 

rates from 2016 is consistent with the capping of interest rates in Kenya, which went into effect that same 

year (Central Bank of Kenya/CBK 2018). According to the new rule, the maximum lending rate cannot be 

more than 4% over the base rate set by the central bank (CBK 2018). 

However, the interest rate caps on commercial loans were lifted in 2019, and this is shown by an 

upturn in interest rates in Figure 1 (IMF 2021). The goal of eliminating interest rate ceilings in 2019 was to 

facilitate greater credit expansion and to stimulate private investment (IMF 2021). Despite the repeal, 

lending rates charged by banks have not increased significantly over the prior cap rate. In 2023, as part of an 

ongoing set of reforms, the Kenyan central bank established a new interest rate corridor to guide the 

-10

-5

0

5

10

15

20

25

30

35

40

1980 1985 1990 1995 2000 2005 2010 2015 2020

A
n

n
u

a
l 

%

Interest rates GDP per capita growth



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

73 

 

overnight interbank rate and reduce the premium for its discount (IMF 2024). The initiative was undertaken 

to further strengthen the monetary policy implementation framework. From the economic growth front, the 

notable downward spike in GDP per capita between 1990 and 1992 can be linked to the suspension of the 

balance of payments assistance from bilateral donors, while the downward spike in the 2019-20 period is 

attributable to the COVID-19 pandemic shock and ongoing volatility in the global financial system (see also 

IMF 2022).  

 

METHODOLOGY 

 

Data description 

The study used annual time-series data from 1980 to 2021. The availability of reliable and consistent 

time-series data for each of the model variables for the study country influenced the selection of the data 

range. The data used in this study were obtained from the World Bank's online database. Table 1 presents 

the definition of variables, measurements, and expected signs. 

 

 Table 1. Definitions of variables and data sources 

Variables Definitions of variables  

(Measurements) 

A priori expectation 

Economic growth (Y) GDP per capita (constant 2015 

US$) 

Dependent variable 

Interest rate (INT) Lending interest rate (%) +/- 

Investment (INV) Gross fixed capital formation (% of 

GDP) 

+ 

Financial development (CRED) Domestic credit to private sector (% 

of GDP) 

+ 

Trade openness (TO)  Trade (% of GDP) +/- 

Exchange rate (EXC) Official exchange rate (LCU per 

US$, period average) 

+/- 

 

Model specification 
The baseline model used in this study is defined as follows: 

Yt = f(INT, INV, CRED, TO, EXC) (1) 

Where Table 1 defines each variable. 

 

In order to obtain elasticity coefficients on the variables and minimise the impact of outliers, the 

variables are converted to logarithms. Therefore, equation (1) is specified as follows: 

lYt = α0 + β1lINTt + β2lINVt + β3lCREDt + β4lTOt + β5lEXCt + μt (2) 

There is a vast array of literature that attempts to theorise the numerous causes of economic growth. 

The core tenet of the Harrod-Domar (H-D) model of economic growth is that increased production levels at 

the micro and macro levels are the result of a progressive accumulation of additional physical capital 

financed by savings and investments (Nguyen 2023). Subsequent to the H-D model is the Solow-Swan 

model, which emphasises the smooth substitution between capital and labour (Nguyen 2023). Early in the 

20th century, financial depth and trade openness were recognised as crucial components of economic growth 

(Odhiambo and Saungweme 2023a). Nyasha et al. (2021), use GDP per capita as the dependent variable to 

facilitate cross-country comparisons of different population sizes. In other words, GDP per capita adjusts 

nominal GDP for changes in price levels and population growth. 

The lending interest rate is the primary independent variable in this study. Theoretically, interest rates 

are regarded as the costs of borrowing investment and consumption funds from financial institutions. Bank 

lending rates are expected to either have a positive or negative relationship with economic growth. A 

decrease in lending rates is expected to induce borrowing for investment and consumption, hence leading to 

economic growth (Adabor 2022; Foldvary 2015). However, an increase in lending interest rates can lead to 

a decline in economic growth as it increases the cost of borrowing, which might discourage borrowing for 

investment and consumption, which is needed to grow the economy. 

The incorporated control variables in model 1, namely investment, financial development, trade 



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

74 

 

openness, and exchange rate, are buttressed by both theory and prior empirical evidence; hence, it is 

anticipated that their coefficients will be statistically significant. The Solow-Swan model states that 

investment variations have an impact on total production up until steady-state per capita income is reached. 

This suggests that while investment has a significant role in determining growth in the short term, its impact 

on national output is essentially neutral over the long term. Empirical studies consistent with a positive 

relationship between investment and economic growth include Odhiambo and Saungweme (2023b) and 

Ibrahimov et al. (2023). 

The inclusion of financial development in the model is due to the perceived positive spillover effects 

in an economy reliant on knowledge and technology, which ultimately leads to economic growth (Giri et 

al. 2023). Furthermore, the underlying theory supporting the inclusion of trade openness in the baseline 

model argues that trade openness either amplifies or attenuates the effects of globalisation, which in turn 

encourages technology transfer and foreign direct investment, which eventually influences economic growth 

(Chen et al. 2020). However, Baliamoune-Lutz and Ndikumana (2007), in their panel data analysis from 39 

African countries covering the period 1975-2001, found evidence in support of a negative relationship 

between trade openness and economic growth. This relationship was caused by weak institutions in the 

studied economies. 

The role of exchange rates in influencing economic growth cannot be understated, particularly in an 

analysis of a commodity-exporting country, such as Kenya. Overvalued currencies can be linked to 

macroeconomic instability, rent-seeking and corruption, unsustainable high current account deficits, foreign 

exchange shortages, and balance of payments crises (Rodrik 2008). All these factors are detrimental to 

economic growth. 

 

Estimation techniques 

This study applies a nonlinear autoregressive distributed lag (NARDL) model. The NARDL 

model explicitly captures the short-run and long-run asymmetries (positive and negative shocks) in lending 

interest rates on economic growth—a major advantage over linear vector autoregressive models. The 

cumulative dynamic multipliers of the NARDL model further explain the speed with which economic 

growth returns to equilibrium following a negative or positive shock in lending interest rates (Shin et al. 

2014). The superiority of NARDL over other competing techniques is its ability to give reliable coefficients 

even in small samples, account for short-run volatilities and structural break problems in the data, account 

for endogeneity among all the variables, and its applicability to data with mixed orders of integration of at 

most one (Shin et al. 2014). 

Thus, following Shin et al. (2014), interest rates can be decomposed into partial sums of positive 

changes and negative changes, making it possible to examine the marginal impact of the two components 

on economic growth in Kenya. This gives the following expression: 

lYt = η+lINTt
+ + η−lINTt

− + Zt + ξ1t (3) 

where:  

𝑙𝐼𝑁𝑇𝑡
+ = ∑ ∆𝑙𝐼𝑁𝑇𝑡

+
𝑡

𝑘=1
= ∑ max(∆𝑙𝐼𝑁𝑇𝑘 ; 0)

𝑡

𝑘=1
 (4) 

𝑙𝐼𝑁𝑇𝑡
− = ∑ ∆𝑙𝐼𝑁𝑇𝑡

−
𝑡

𝑘=1
= ∑ min(∆𝑙𝐼𝑁𝑇𝑘 ; 0)

𝑡

𝑘=1
 (5) 

 

Where ∆ change, Z is a set of  control variables, 𝜉1𝑡  is white noise error term. 
Using equations (4) and (5), the NARDL framework as defined by Shin et al. (2014) is specified as: 

 

∆𝑙𝑌𝑡 = 𝜅0 + ∑ 𝜆1𝑖∆𝑙𝑌𝑡−𝑖 +

𝜌

𝑖=1

∑ 𝜆2𝑖
+ ∆𝑙𝐼𝑁𝑇𝑡−𝑖

+ + 

𝜐1

𝑖=0

∑ 𝜆3𝑖
− ∆𝑙𝐼𝑁𝑇𝑡−𝑖

− + 

𝜐2

𝑖=0

∑ 𝜆4𝑖∆𝑙𝐼𝑁𝑉𝑡−𝑖

𝜐3

𝑖=0

+ ∑ 𝜆5𝑖

𝜐4

𝑖=0

∆𝑙𝐶𝑅𝐸𝐷𝑡−𝑖 + ∑ 𝜆6𝑖∆𝑙𝑇𝑂𝑡−𝑖

𝜐5

𝑖=0

+ ∑ 𝜆7𝑖∆𝑙𝐸𝑋𝐶𝑡−𝑖

𝜐6

𝑖=0

+ 𝜓1𝑙𝑌𝑡−1

+ 𝜓2
+𝑙𝐼𝑁𝑇𝑡−1

+ + 𝜓3
 −𝑙𝐼𝑁𝑇𝑡−1

− + 𝜓4𝑙𝐼𝑁𝑉𝑡−1 + 𝜓5𝑙𝐶𝑅𝐸𝐷𝑡−1 +  𝜓6𝑙𝑇𝑂𝑡−1

+  𝜓7𝑙𝐸𝑋𝐶𝑡−1 + 𝜉2𝑡 

(6) 

Where 𝜌;  𝜐1 − 𝜐6 is optimal lag order, 𝜅0 is constant, 𝜆1, 𝜆4, 𝜆5, 𝜆6, 𝜆7 𝑎𝑛𝑑 𝜆8 are short-run 



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75 

 

coefficients, 𝜆2𝑖
+  𝑎𝑛𝑑 𝜆2𝑖

−  are short-run asymmetric distributed lag parameters, 𝜓1, 𝜓4, 𝜓5, 𝜓6, 𝜓7𝑎𝑛𝑑 𝜓8 are 

long-run coefficients, 𝜓2
+ 𝑎𝑛𝑑 𝜓2

− are long-run asymmetric distributed lag parameters, 𝜉2𝑡  is white noise error 
term, t is time period and l is natural logarithmic transformation. All other variables are as defined in Table 

1. 

Cointegration in a NARDL model setting is ascertained by comparing the computed F-statistic to the 
upper and lower critical bounds from Pesaran et al. (2001) critical values. The asymmetric impact of  lending 

interest rates on economic growth in Kenya is ascertained if  𝜓2
+ ≠ 𝜓3

 −. If  this condition holds, then the 

following error correction model (ECM) is specified: 

 

 

𝑙𝑌𝑡 = 𝜅0 + ∑ 𝜆1𝑖∆𝑙𝑌𝑡−𝑖 +

𝜌

𝑖=1

∑ 𝜆2𝑖
+ ∆𝑙𝐼𝑁𝑇𝑡−𝑖

+ + 

𝜐1

𝑖=0

∑ 𝜆3𝑖
− ∆𝑙𝐼𝑁𝑇𝑡−𝑖

− + 

𝜐2

𝑖=0

∑ 𝜆4𝑖∆𝑙𝐼𝑁𝑉𝑡−𝑖

𝜐3

𝑖=0

+ ∑ 𝜆5𝑖

𝜐4

𝑖=0

∆𝑙𝐶𝑅𝐸𝐷𝑡−𝑖 + ∑ 𝜆6𝑖∆𝑙𝑇𝑂𝑡−𝑖

𝜐5

𝑖=0

+ ∑ 𝜆7𝑖∆𝑙𝐸𝑋𝐶𝑡−𝑖 + 𝜙𝐸𝐶𝑀𝑡−1 +  𝜉3𝑡  

𝜐7

𝑖=0

 

 

(7) 

Where 𝜙 is coefficient of  the error term and ECM is error correction term. 

 

To confirm convergence to long-run equilibrium following a shock or short-term disequilibrium, the 

coefficient of  the error correction term (𝜙) is anticipated to be negative and statistically significant, lying 

between 0 and 1.  

The current paper makes use of  time series data, so it is necessary to pre-test each variable for unit 

root in order to prevent spurious regressions and to determine the order of  integration for each variable. 
According to Pesaran et al. (2001) and Shin et al. (2014), the NARDL model requires that no variable be 

integrated of  an order higher than one. To distinctly determine the order of  integration, the paper uses three 

techniques, namely, the Dickey-Fuller Generalised Least Square (DF-GLS), Phillips-Perron (PP) and Zivot-

Andrews (ZAURoot) techniques. The paper incorporates the ZAURoot technique so as to correct for 

structural breaks and, therefore, correctly determine the order of  integration among the variables.   

After ascertaining the order of  integration for each variable, the paper then conducts a cointegration 

test to ascertain the applicability of  the NARDL process. The paper also undertakes nonlinearity tests in the 

series using the BDS test. The null hypothesis of  linearity, under various BDS dimensions (m = 2, 3, 4, 5, 6), 

is put to the test. The Wald test is also included in this paper to check for both short-run and long-run 

asymmetries. After undertaking these preliminary checks, the paper proceeds to estimate the asymmetrical 

long- and short-run effects of  lending interest rates on economic growth using equations 5 and 6. Finally, 

the paper carries out post-diagnostic tests, including the recursive CUSUM and CUSUMSQ tests, to check 

the null hypothesis that the parameters are unstable. The dynamic multiplier tests further show graphically 

the rate of  response of  economic growth to positive and negative variations in interest rates (see Shin et al., 

2014). 

 

RESULTS AND DISCUSSION 

 

Nonlinearity and stationarity results 
The study first determines whether the series has a nonlinear relationship. The findings are shown in 

Table 2. There is evidence indicating the existence of a nonlinear relationship between the series in Table 2. 

This result is confirmed by the BDS test statistics for each variable, which were found to be statistically 

significant at 1% across all dimensions. Panels A, B, and C of Table 3 provide a summary of the three 

stationarity test results. The results of the DF-GLS test show that investment is stationary at all levels [I(0)], 

while GDP per capita, trade openness, and exchange rate are all conclusively stationary after first 

differencing [I(1)]. According to the findings of the PP and ZAURoot tests, all series are conclusively 

integrated of order one (1). Overall, the stationarity results indicate that the order of integration of the 

variables is a mixture of not more than 1. This attests to the appropriateness of using the bounds test to 

investigate the long-run relationship between interest rates and GDP per capita in Kenya.    

 

  



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

76 

 

Table 2. BDS Test results for nonlinearity 

Variables BDS Statistic 

Dimension 2 Dimension 3 Dimension 4 Dimension 5 Dimension 6 

BDS 

Statistic 

P-

value 

BDS 

Statistic 

P-

value 

BDS 

Statistic 

P-

value 

BDS 

Statistic 

P-

value 

BDS 

Statistic 

P-

value 

lY 0.153*** 0.000 0.239*** 0.000 0.274*** 0.000 0.279*** 0.000 0.251*** 0.000 

lINT 0.146*** 0.000 0.237*** 0.000 0.290*** 0.000 0.318*** 0.000 0.326*** 0.000 

lINV 0.070*** 0.000 0.100*** 0.000 0.110*** 0.000 0.107*** 0.000 0.093*** 0.000 

lCRED 0.105*** 0.000 0.153*** 0.000 0.180*** 0.000 0.184*** 0.000 0.170*** 0.010 

lTO 0.132*** 0.000 0.193*** 0.000 0.201*** 0.000 0.169*** 0.000 0.113*** 0.000 

lEXC 0.203*** 0.000 0.343*** 0.000 0.442*** 0.000 0.510*** 0.000 0.558*** 0.000 

Notes:  *** denote statistical significance at 1% leve1. 

 

Table 3. Stationarity results  

 Panel A: Dickey-Fuller Generalised Least Square (DF-GLS) 

Variable Level First Difference 

Without Trend With Trend Without Trend With Trend 

lY 1.488 -0.434 -4.021*** -4.814*** 

lINT -1.217 -1.425 -4.591*** -5.611*** 

lINV -2.774*** -2.980* ___ ___ 

lCRED -1.196 -3.094* -7.389*** -7.882*** 

lTO -0.605 -1.974 -6.602*** -6.608*** 

lEXC 0.086 -1.073 -4.368*** -5.705*** 

 Panel B: Phillips-Perron (PP) 

 Level First Difference 

Without Trend With Trend Without Trend With Trend 

lY 1.419 -0.323 -3.949*** -4.710*** 

lINT -1.862 -2.061 -5.432*** -5.606*** 

lINV -2.732* -2.890 -11.123*** -11.332*** 

lCRED -1.102 -3.521* -8.038*** -7.958*** 

lTO -0.888 -1.848 -6.605*** -6.655*** 

lEXC -3.207** -1.772 -5.039*** -5.615*** 

 Panel C: Zivot-Andrews (ZAURoot) 

 Level First Difference 

Without 

Trend 

Break With 

Trend 

Break Without 

Trend 

Break With 

Trend 

Break 

lY -2.466 1992 -3.339 2000 -5.441*** 1991 -5.730*** 1990 

lINT -2.936 1989 -3.177 1999 -6.836*** 1995 -7.043*** 1995 

lINV -4.198 1996 -4.261 1996 -6.443*** 2000 -6.415*** 2000 

lCRED -3.765 2006 -4.018 2006 -6.464*** 1991 -7.196*** 2013 

lTO -3.635 2015 3.814 2010 -6.906*** 1988 -7.147*** 1995 

lEXC -4.254 1991 -6.761*** 1993 -6.276*** 1995 -6.571*** 1994 

Notes: ** and *** denotes statistical significance at 5% and 1% level. 

 

Cointegration and asymmetric test results 
Table 4 displays the outcomes of the cointegration tests carried out utilising the NARDL bounds 

testing methodology. The results show that the F-statistic value for the NARDL model is 4.215 and is 

statistically significant at the 5% level. This suggests that the variables in the nonlinear model have a 

cointegrating correlation. The Wald test results presented in Table 5 firmly reject the null hypothesis of long-

run and short-run symmetry. This is confirmed by the associated long-run and short-run p-value of the Wald 

F-statistic (WLR), which is statistically significant at 1%, and WSR, which is significant at the 10% level. This 

finding implies that interest rates have a distinct long-run and short-run asymmetric effect on economic 

growth in Kenya.  

 

 



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

77 

 

Table 4. Bounds F-test results for cointegration – NARDL   

F-Statistic Cointegration Status 

4.215** Cointegrated 

Pesaran et al. 

(2001), p.300, 

Table CI(iii) 

Case III 

Asymptotic critical values for 

10% 5% 1% 

I(0) I(1) I(0) I(1) I(0) I(1) 

2.53 3.59 2.87 4 3.6 4.9 

Notes: **denotes statistical significance at 5% level. 

 

Table 5. Wald test results 

Test  F-statistic P-value Decision 

WLR  9.201*** 0.007 Asymmetric 

WSR  4.046* 0.056 Asymmetric 

Notes: WLR is long-run asymmetric test; WSR is short-run asymmetric test; *** and * signifies significance at 

1% and 10% level. 
 

Long-run and short-run NARDL results 

Table 6, panels A and B, presents the long-run and short-run NARDL results, respectively.   

 

Table 6. NARDL results - long-run and short-run coefficients  

Dependent Variable is Y 

Panel A: Long-Run Results   

Regressor Coefficient T-ratio [p-value] 

lINT+ -0.086 -0.980 [0.339] 

lINT− 0.461*** 4.399 [0.000] 

lINV -0.043 -0.269 [0.791] 

lCRED 0.066 0.570 [0.575] 

lTO 0.056 0.480 [0.637] 

lEXC -0.322*** -4.174 [0.001] 

Panel B: Short-Run Results                                                                                                      

Regressor Coefficient T-ratio [p-value] 

C 4.229*** 6.225 [0.000] 

@Trend 0.031*** 6.335 [0.000] 

∆lINT+ 0.030 0.721 [0.480] 

∆lINT− 0.041 1.278 [0.217] 

∆lINT−(−1) -0.178*** -3.989 [0.001] 

∆lINT−(−2) -0.120*** -3.297 [0.004] 

∆lINV 0.057* 1.817 [0.085] 

∆lINV(−1) 0.131*** 4.327 [0.000] 

∆lCRED -0.089*** -3.097 [0.006] 

∆lCRED(−1) -0.076** 2.408 [0.026] 

∆lTO 0.056* 2.052 [0.054] 

∆lTO(−1) -0.061** -2.254 [0.036] 

∆lEXC -0.183*** -3.972 [0.001] 

ECM(−1) -0.567*** -5.904 [0.000] 

Panel C: Test statistics 

R- Squared 

R-Bar-Squared 

F-Statistic [Prob] 

Normality  

Serial Correlation 

Heteroscedasticity 

Functional Form 

0.793 

0.693 

7.959 [0.000] 

0.779 [0.677] 

0.969 [0.400] 

1.007 [0.493] 

0.879 [0.361] 

Notes: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively “+” and “-” 

denotes positive and negative shocks. 

 



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

78 

 

The NARDL results, which are reported in Table 6, indicate that positive changes in interest rates 

(𝑙𝐼𝑁𝑇+) have no significant impact on economic growth, irrespective of whether the analysis is conducted 

in the short run or in the long run. The results also indicate that negative changes in interest rates (𝑙𝐼𝑁𝑇−) 

and economic growth move in the same direction, as evidenced by the positive and statistically significant 

coefficient of the negative partial sum of interest rates. This suggests that, other things being equal, negative 

shocks to interest rates are likely to be accompanied by a corresponding decline in economic growth in the 

long run. One reason for this could be that when the central bank lowers interest rates, economic agents save 

less because they will not be earning higher returns on their savings. The low saving rate can lead to a lower 

level of investment, which could contribute to a decrease in economic growth. 

The results also indicate that there is an inverse relationship between negative changes in lending 

interest rates from the preceding period and economic growth in the short run. This is supported by the 

coefficient of the partial negative sum of interest rates, which has been found to be negative and statistically 

significant. This indicates that decreases in interest rates have the potential to spur economic growth in the 

short run, as lower rates can incentivise borrowing for both investment and consumption. The major findings 

of this study indicate that negative changes in interest rates have an asymmetrical impact on economic 

growth in Kenya, depending on the direction of change and the time scale taken into account. Overall, based 

on these conclusions, the study concludes that negative interest rate changes play a significant role in 

defining the country's possibilities for future prosperity. 

The results of control variables reported in panels A and B show that investment has a significant 

positive impact on economic growth in Kenya, only in the short run.  This finding is consistent with the 

principles of H-D and Solow-Swan models presented in earlier sections. The findings of financial 

development point to detrimental effects on economic growth exclusively in the short run, while it is 

statistically insignificant in the long run. Trade openness has been shown to promote economic growth in 

the short run, but the results also indicate that trade openness from previous periods causes economic 

decline. While the adverse effects of financial development may indicate a small financial sector vulnerable 

to adverse financial developments in international markets, Baliamoune-Lutz and Ndikumana (2007) 

contend that weak institutions to facilitate reciprocal trade benefits are the root cause of the negative 

correlation between trade openness and economic growth (see also, IMF, 2024). Furthermore, it was shown 

that changes in exchange rates have a negative impact on economic growth in the long and short run. This 

implies that a depreciation of the exchange rate in Kenya is likely to boost exports and increase economic 

growth.  

The dynamic multiplier graph presented in Figure 2 validates the presence of an asymmetric 

relationship between interest rates and economic growth. Explicitly, the black dotted line indicates the non-

linear adjustment of economic growth to negative shocks, while the solid black line portrays the adjustment 

of economic growth to positive shocks. Overall, the dynamic multiplier reported in Figure 2 shows that the 

effects of positive shocks to interest rates are more pronounced than those of negative shocks in the long run. 

The CUSUM and CUSUMSQ graphs presented in Figure 3 are within the bounds at a 5% significance 

level, implying that the estimated model passes the stability test, confirming the consistency and reliability 

of the coefficients. 

 

 
Figure 2. Dynamic multiplier graph 

 

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

1 3 5 7 9 11 13 15

Multiplier for LINT(+)

Multiplier for LINT(-)

Asymmetry Plot (with C.I.)



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

79 

 

 
  

Figure 3a. CUSUM graph    Figure 3b. CUSUMSQ graph 

 

CONCLUSION AND RECOMMENDATIONS 

 
The economic and monetary policies of Kenya underwent significant evolution from 1964 to 2023. 

Initially characterised by dormant interest rate policies managed through statutory controls, the landscape 

shifted by 1991 as internal and external shocks prompted economic adjustments emphasizing trade 

liberalisation and interest rate deregulation. Therefore, this study extended the investigation of the 

asymmetric impact of lending interest rates on economic growth in Kenya using a nonlinear ARDL model 

and annual time-series data spanning the years 1980-2021. Unlike some of the previous studies, which used 

linear models, this research employed the NARDL framework to examine the short- and long-run 

asymmetric impact of lending interest rates on economic growth in Kenya, providing an intricate 

understanding of the interest rate-growth nexus. The findings underscore the existence of an asymmetric 

long-run relationship, as evidenced by cointegration and dynamic asymmetry tests. Specifically, while 

positive shocks in lending interest rates exhibit no significant impact on economic growth, negative interest 

rate changes are shown to spur growth in the short run but impede it in the long run. Short-run benefits of 

lower interest rates stem from increased borrowing for productive investments, enhanced consumption, and 

the stimulation of export-driven industries such as agriculture and manufacturing, owing to currency 

depreciation. Conversely, prolonged interest rate reductions may deter savings and compromise long-term 

investment, posing challenges to sustained economic growth. Investment, financial development, trade 

openness, and exchange rates were found to play critical roles in moderating the growth trajectory, though 

their effects varied across time horizons.  

Policy suggestions from this paper are: (1) Policymakers should carefully balance short-term stimulus 

policies with long-term sustainability by avoiding extreme interest rate fluctuations. Short-term measures 

should focus on reducing lending rates during economic slowdowns to encourage borrowing for investment, 

entrepreneurship, and job creation. (2) Given that investment positively affects economic growth in the short 

run, particularly with a lag, policymakers should implement short-term measures to stimulate both private 

and public investments, such as subsidies and credit facilities. Since investment also contributes to long-term 

economic growth, authorities should establish sustainable financing mechanisms, such as public-private 

partnerships for infrastructure projects. (3) As credit to the private sector has a negative impact on economic 

growth in the short run, financial regulators should closely monitor excessive lending to ensure that credit 

allocation supports productive sectors rather than speculative activities. Authorities should promote 

responsible lending through macro-prudential policies while also developing robust credit risk management 

frameworks and fostering financial literacy to enhance the positive long-run effects of financial 

intermediation. (4) Since exchange rate depreciation negatively affects economic growth in both the short 

and long run, the central bank should continuously monitor exchange rate fluctuations and, when necessary, 

implement appropriate monetary policies - within the bounds of its existing free-floating exchange rate 

system - to smooth out extreme and undesirable movements. (5) Export diversification strategies should be 

encouraged to reduce vulnerability to external shocks and enhance economic resilience. 

Future studies on the subject should extend the analysis to estimate the threshold point of lending 

interest rates that would set the country on an optimal growth path. Additionally, the scope of analysis 

should be broadened to include other African economies to uncover regional trends and policy implications.  

 

-15

-10

-5

0

5

10

15

2004 2006 2008 2010 2012 2014 2016 2018 2020

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

2004 2006 2008 2010 2012 2014 2016 2018 2020

CUSUM of Squares 5% Significance



T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

80 

 

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