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American Finance & Banking Review; Vol. 5, No. 1; 2020 
ISSN 2576-1226    E-ISSN 2576-1234 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 
 

     62 
 

 

Financial Conditions Index and Economic Performance in Nigeria 
 
 
 
 

Chukwu Agwu Ejem PhD 
Senior Lecturer 

Department of Banking and Finance 
Abia State University, Uturu, Nigeria 

E-mail: ecjah71@yahoo.com 
 

Udochukwu Godfrey Ogbonna PhD 
Senior Lecturer 

Department of Management Science 
Rhema University, Aba, Nigeria 

E-mail: kellyogbo2004@yahoo.com 
 
 
Abstract 
The main aim of this study is to construct a financial conditions index for Nigeria and analyze its predictive power for future 
growth rate and inflationary trend. The study is based on yearly time series data from 1985 to 2018. The variables included in 
the construction of the index are riskless interest rate, stock market index, exchange rate, credit to private sector and interest rate 
spread. The weights attached to these variables are derived from ARDL coefficients, while the predictive power of the 
constructed index is examined within the VAR framework. The results from the ARDL model shows that credit to private 
sector and stock market index are the most significant factors for nominal GDP, hence having a substantial weight in the 
resultant financial conditions index. However, the results from VAR impulse response function and forecast error variance 
decomposition suggest that the constructed financial conditions index contain very little predictive information about future 
growth rate and inflationary trend.  
 
Keywords: Financial Conditions Index, Inflation, Real GDP Growth Rate.  
 
1. Introduction 
Understanding how monetary policy shocks transmit to the real economy is an age long issue, and has continued to attract 
scholarly debate in both developing and developed countries. Traditionally, changes in monetary policy stance affect savings and 
investment behaviours of both households and firms through interest rates and exchange rate. Hence, monetary conditions index, 
which reflects changes in monetary conditions related to interest rate and exchange, was used during the 1990s to track monetary 
policy stance.  

However, the rapidly growing complexities of financial systems all over the world have necessitated a more 
comprehensive index that accommodates the impact of financial markets in the monetary policy transmission mechanism 
(Swiston, 2008). More specifically, since early 2000s, there is a growing body of evidence suggesting that financial conditions 
index provides a more useful tool for forecasting the performance of real economic variables, especially, where interest rates and 
exchange rate are found insufficient (Angelopoulou, Balfoussia& Gibson, 2014).Therefore, financial conditions index is,not only 
a natural extension of monetary conditions index, but also, a more comprehensive index that accommodates the importance of 
asset prices (equity and property prices) in the monetary policy transmission mechanism (Angelopoulou, Balfoussia& Gibson, 
2014).  

This study constructs a financial conditions index for Nigeria and examining its predictive power for both inflation 
and growth rate in real GDP using yearly time series data from 1985 to 2018. While the financial conditions index was 
constructed based on ARDL model which is novel in the literature, its predictive power for inflation is examined based on 
structural vector autoregressive model.  

The remainder of this study has the following structure: The next section reviews the extant literature on finance-
growth relationship as well as financial conditions index. Section 3 describes the data, models and methods; section 4 contains 
empirical analysis and discussion while the study is concluded in section 5.  

mailto:ecjah71@yahoo.com


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2. Literature Review 
2.1 Financial Conditions Indicators and Economic Growth  
The relationship between financial conditions indicators and economic growth can be examined in the context of supply leading 
theory. The supply leading theory, which was initially due to Schumpeter (1912), and popularized by Levine and King (1997), 
states financial development is a precursor for and causes economic growth. If savings are efficiently mobilized and channelled 
into productive activities, there would be a significant increase in economic growth. Although, numerous studies (both in Nigeria 
and other developing and developed countries) have subjected this theoretical assertion to empirical tests, there is not yet a 
consensus. However, recent evidence reported in the empirical literature tends to support the view that financial sector 
development can trigger economic growth. These studies include Ipeghan and Marshall (2019), Olaniyan(2019), Ncanywa and 
Mabusela(2019), Olayungbo and Quadri(2019) and Okunlola, Masade, Folaranmi Lukman and Ajayi Abiodun(2020).  

Ibrahim, Abdullahi, Azman-Saini and Rahman (2017) employed the dynamic panel GMM method to investigate the 
effects of both banking sector and stock market developments on economic growth for 53 countries from 1988 to 2012. 
Consistent with the supply-leading hypothesis, they found that both stock market and banking sector developments are necessary 
ingredients for economic growth.  

Ipeghan and Marshall (2019) contributed to the finance-growth literature by empirically testing the effects capital 
market and credit market on economic growth using Nigerian data at yearly frequency spanning from 1981 to 2017. Using the 
Johansen cointegrating framework and pairwise Granger causality test, they found that both capital market and banking sector 
development scan significantly affect, and are cointegrated with, economic growth.  

Olaniyan(2019) used the instrumental generalized method of moment approach to examine the finance-growth 
relationship in Nigeria from 1977 to 2017. He incorporated the interactive effect of remittances and financial development in 
the growth model and argues that it can improve growth estimate. Fitting the resultant growth model to yearly data obtained 
from the World Development Indicators database reveals that although, both remittances and financial development (credit to 
private sector ratio to GDP) individually affect economic growth negatively, their interaction plays a positive and highly 
significant role in the growth model.  

Ncanywa and Mabusela(2019)employed the dynamic panel ARDL framework to test the link between financial 
development and economic growth in five sub-Saharan African countries; namely, Ghana, Kenya, Botswana, Nigeria and South 
Africa, using yearly data from 1980 to 2014. They found the presence of both short run and long run relationship between 
financial development and economic growth, with credit to private sector and bank liquid liabilities both exerting a positive long 
run effect on economic growth, while domestic savings exerts a negative effect.  

Olayungbo and Quadri(2019)employed both the Pooled Mean Group ARDL framework to model the relationship 
between financial development and economic growth incorporating the both the direct and interaction effect of remittances. The 
study focuses on sub-Saharan countries and is based on yearly data for 20 countries from 2000 to 2015 which were collected 
from the World Development Indicators data based. They found amongst others that both financial development and 
remittances exhibit a positive effect on economic growth.  

More recently, Okunlola, Masade, Folaranmi Lukman and Ajayi Abiodun(2020)examined the finance-growth nexus in 
Nigeria using the causality framework developed by Toda and Yamamoto (1995) which is based on augmented VAR. Based on 
yearly data from 1985 to 2015 collected from CBN, NBS and NSE, they find evidence supporting the supply-leading theory 
which asserts that the level of financial development matters for economic growth.  

 
2.2 Financial Conditions Index and Economic Performance 
There is a growing body of literature on the relationship between financial conditions index and the behaviour of real economic 
variables. What appears to be the emerging consensus is that financial conditions index can usefully predict future direction of 
economic variables such as growth rate in real GDP and level of inflation. However, it appears that no recent empirical study has 
focused in line of inquiry in Nigeria.  

Swiston (2008) employs VAR based impulse-response functions to construct a financial conditions index for US. 
They found that credit availability is an important driver of the business cycle, and accounts for more than 20% of the typical 
contribution of financial variables to economic growth. They also found that the constructed financial conditions index is a 
precise predictor of real GDP growth.  

Khundrakpam, Kavediya and Anthony (2017) constructed financial condition indices for India and evaluated their 
ability to predict business cycle. They examined whether financial conditions index constructed based on principal component 
analysis (PCA) can predict economic growth rate better than financial conditions index constructed based on vector 
autoregressive (VAR) model. Their results show that PCA-based financial conditions index outperforms VAR-based financial 
conditions index.  

Using a time-varying factor augmented vector autoregressive models with stochastic volatility Wang, Xu and Chen 
(2018) constructed a financial conditions index for China and examine its relationship with future inflation. Based on monthly 



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64 
                         
 

data spanning from January 2002 to January 2015, they found that financial conditions index is useful in predicting future 
inflation.  

Kabundi and Mbelu (2020) constructed a financial conditions index for South Africa using monthly time series data 
January 2000 to April 2017. The index was constructed based on time-varying principal component factor model proposed by 
Koop and Korobilis (2014). They also specified and estimated a three-factor time-varying parameter factor-augmented vector 
autoregressive (TVP-FAVAR) model incorporating financial conditions index, headline inflation and GDP growth rate. They 
found amongst others that tighter financial conditions reduce both economic growth rate and level of inflation.  
 
3. Empirical Strategy  
3.1 Data Description 
In this study, yearly time series data from 1985 to 2018 are used. The study variables are inflation, nominal GDP, real GDP 
growth rate, riskless interest rate (Treasury bill rate), stock market index (All share indexes), average official exchange rate, credit 
to private sector and interest rate spread (prime lending rate minus monetary policy rate). All data were sourced from the annual 
statistical bulletin of the Central Bank of Nigeria and are analyzed using the E-Views software package.  
Consistent with the extant literature, we construct the financial conditions index using the following formula.  

𝐹𝐶𝐼 =∑𝑤𝑗

𝑛

𝑗=1

(𝑥𝑗,𝑡 − 𝑥�̅�) 

Where FCI = financial conditions index, 𝑥𝑗,𝑡 = financial conditions variable j at time t, 𝑥�̅� = mean of 𝑥𝑗  and 𝑤𝑗 is the 

weight attached to each 𝑥𝑗 . Here, 𝑥𝑗  includes riskless interest rate (TBR), stock market index (ASI), exchange rate (EXR), credit 

to private sector (CPS) and interest rate spread (SPREAD).  
Figure 1 shows the time series plot of the data. As this Figure shows, while nominal GDP, average official exchange 

rate, stock market index and credit to private sector show an upward trend, real GDP growth rate, inflation, and interest rate 
spread appear to be stationary. Riskless interest rate appears to follow a random walk with drift, increasing initially up to 1993 
but showed a steady decline with observable fluctuations. However, it showed an upward trend from 2009. All these suggest that 
our variables have different levels of integration; hence there is good reason to model the relationship between financial 
conditions variables and economic growth within an ARDL framework.  

-4

0

4

8

12

16

1985 1990 1995 2000 2005 2010 2015

GDPG

0

20

40

60

80

1985 1990 1995 2000 2005 2010 2015

INFL

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1985 1990 1995 2000 2005 2010 2015

GDP

0

5

10

15

20

25

30

1985 1990 1995 2000 2005 2010 2015

TBR

0

40

80

120

160

200

240

280

320

1985 1990 1995 2000 2005 2010 2015

AOEXR

0

10,000

20,000

30,000

40,000

50,000

60,000

1985 1990 1995 2000 2005 2010 2015

ASI

0

5,000

10,000

15,000

20,000

25,000

1985 1990 1995 2000 2005 2010 2015

CPS

-10

-5

0

5

10

15

1985 1990 1995 2000 2005 2010 2015

SPREAD

 
Figure1. Time Series Plot for the Study Variables 



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65 
                         
 

3.2 Methods 
To the estimate the relative impact of each financial conditions variable on nominal GDP, we employ the Autoregressive 
Distributive Lag (ARDL) framework. Compared with other dynamic time series frameworks, this dynamic estimation 
framework, which is novel in the literature in the context of constructing a financial conditions index, is employed basically 
because of its ability to accommodate time series variables that have different orders of integration in the same empirical model  
We specify our simple ARDL models as follows:  
 

𝐺𝐷𝑃𝑡 = 𝛼0 + 𝛼1𝐺𝐷𝑃𝑡−1 + 𝛼2𝑇𝐵𝑅𝑡 + 𝛼3𝑇𝐵𝑅𝑡−1 + 𝛼4𝐴𝑆𝐼𝑡 + 𝛼5𝐴𝑆𝐼𝑡−1 + 𝛼6𝐸𝑋𝑅𝑡 + 𝛼7𝐸𝑋𝑅𝑡−1 + 𝛼8𝐶𝑃𝑆𝑡
+ 𝛼9𝐶𝑃𝑆𝑡−1 + 𝛼10𝑆𝑃𝑅𝐸𝐴𝐷𝑡 + 𝛼11𝑆𝑃𝑅𝐸𝐴𝐷𝑡−1 + 𝜖𝑡 

 

Where 𝛼0 is the regression intercept and 𝛼1 is the autoregressive coefficient capturing the effect of lagged nominal 

GDP. Further, 𝛼2 and 𝛼3 are model coefficients that capture the concurrent and lagged effects of riskless interest rate, 𝛼4 and 

𝛼5 are model coefficients that capture the concurrent and lagged effects of stock market index, 𝛼6 and 𝛼7 are model coefficients 

that capture the concurrent and lagged effects of exchange rate, 𝛼8 and 𝛼9 are model coefficients that capture the concurrent and 

lagged effects of credit to private sector, 𝛼10 and 𝛼11 are model coefficients that capture the concurrent and lagged effects of 

interest rate spread, and 𝜖𝑡 is the classical disturbance term.  
For each variable, we use the total effect (sum of the concurrent and lagged coefficients) on nominal GDP as its 

relative weight in the construction of financial conditions index. 
To examine whether financial conditions index can predict future economic performance, we employ the vector 

autoregressive (VAR) framework. The three variable reduced form VAR(1) financial conditions index, log of inflation and real 
GDP growth rate is specified as follows: 
 

𝑅𝐺𝐷𝑃𝐷𝑡 = 𝛽01 + 𝛽11𝑅𝐺𝐷𝑃𝐺𝑡−1 + 𝛽21𝐿𝐼𝑁𝐹𝐿𝑡−1 + 𝛽31𝐹𝐶𝐼𝑡−1 + 𝜀1𝑡  
𝐿𝐼𝑁𝐹𝐿𝑡 = 𝛽02 + 𝛽12𝑅𝐺𝐷𝑃𝐺𝑡−1 + 𝛽22𝐿𝐼𝑁𝐹𝐿𝑡−1 + 𝛽32𝐹𝐶𝐼𝑡−1 + 𝜀2𝑡  
𝐹𝐶𝐼𝑡 = 𝛽03 + 𝛽13𝑅𝐺𝐷𝑃𝐺𝑡−1 + 𝛽23𝐿𝐼𝑁𝐹𝐿𝑡−1 + 𝛽33𝐹𝐶𝐼𝑡−1 + 𝜀3𝑡 

 
Although, VAR order is typically selected based on some information criteria, we selected order 1 on rule of thumb 

basis, particularly because of the frequency of our data.  
 
4. Empirical Results and Discussion  
4.1 Summary Statistics 
The descriptive summary of the data is presented in Table 1. exchange The descriptive statistics shows that apart from real GDP 
growth rate, exchange rate and stock market index, all other series have a distribution that significantly deviates from normal 
distribution. 
 
      Table 1. Statistical Summary of the Variables 
 

Variable �̅� Max Min 𝛔 𝐒 𝐊 JB stat 

Growth Rate 4.95 14.60 -1.58 3.81 0.38 2.63 1.0292(0.5977) 

Inflation 19.69 76.76 0.22 18.92 1.68 4.68 19.9173(0.0000) 

Nominal GDP 30794.20 127762.50 192.27 38661.73 1.13 2.94 7.1861(0.0275) 

Treasury Bills Rate 12.44 26.90 3.72 4.59 0.84 4.52 7.2417(0.0268) 

Exchange Rate 101.96 306.92 0.96 90.14 0.74 2.96 3.1346(0.2086) 

Stock Market Index 16298.33 57990.20 127.30 15238.20 0.68 2.71 2.7681(0.2506) 

Credit to Private 
Sector  

5391.61 22521.93 13.07 7650.56 1.17 2.81 7.7921(0.0203) 

Spread 4.79 12.99 -7.68 3.95 -0.62 4.91 7.3185(0.0258) 

  



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4.1 Financial Conditions Variables and Economic Growth 
Table 2 shows the results of the estimated optimal ARDL model for the relationship between financial conditions variables 
(treasury bill rate, stock market index, exchange rate, spread and credit to private sector) and nominal gross domestic product. 
The lag selection for each variable is based on Schwarz information criterion. As stated in the previous section, the estimated 
betas would be used as weightings for the construction of financial conditions index. The estimation is based on Newey-West 
standard errors which are consistent even in the presence of autocorrelation and heteroskedasticity. 

 
Table 2. Short Run and Long Run Results for Finance and Growth; p-values in ( ) 

 

Variable Short Run Coefficient Long Run Coefficient Total 

GDP(-1) 0.4294  (0.0180) –  – 

TBR 0.0612 (0.0851) 0.1074(0.0979) 0.1687 

ASI 0.1908 (0.0035) 0.3345 (0.0000) 0.5253 

EXR 0.0504 (0.0563) 0.0884 (0.1537) 0.1389 

CPS 0.1166 (0.0690) 0.5549 (0.0000) 0.6715 

SPREAD -0.0374 (0.1103) -0.0656 (0.2060) -0.1031 

CointEq(-1) -0.5705 (0.0025) –  – 

Constant  –  2.0663 (0.0000) – 

 
As Table 2 shows, our regression results are striking in many ways. First, the coefficient on GDP(-1)(beta = 0.4294, 

p-value = 0.0180) is positive and statistically different from zero at 5% significance level, indicating that lagged nominal gross 
domestic product is a positive determinant of current nominal gross domestic product. A 1% increase in the nominal GDP in 
the current period would lead to approximately 0.43% increase in the next period nominal GDP, holding the impact of financial 
conditions variables constant. Thus, initial growth level affects the current growth. Secondly, focusing on the short run results, 
we can see that nominal GDP is positively related to riskless interest rate (beta = 0.0612), stock market index (beta = 0.1908), 
exchange rate (beta = 0.0504) and credit to private sector (beta = 0.1166), while it is negatively related to interest rate spread 
(beta = -0.0374). Further, while the effect of stock market index (p-value 0.0035) is highly statistically significant, the effects of 
riskless interest rate (p-value 0.0851), exchange rate (p-value 0.0563) and private sector credit (p-value 0.0690) all are 
significant at 10% level. However, the level of significance of the effect of interest rate spread (p-value 0.1103) exceeds all 
customary levels. These results suggest that relatively, capital market has the highest beneficial impact on nominal GDP, 
followed by private sector credit, and then by riskless interest rate, while exchange rate has the least beneficial impact. However, 
an increase in interest rate spread would marginally reduce nominal GDP.  

Thirdly, the error correction term (CointEq(-1) = -0.5705, p-value = 0.0025) has the expected negative sign, and is 
also quite large and highly statistically significant, indicating that the estimated nominal GDP model can quickly attain 
equilibrium in the long run despite any short run disturbances, and the speed of adjustment is about 57% per annum. Finally, 
the long run results are largely comparable with those of the short run, especially in terms of the signs of the coefficients . 
However, while the long run effects of stock market index (beta = 0.3345, p-value = 0.0000) and credit to private sector (beta 
= 0.5549, p-value = 0.0000) are the most significant in statistical sense, the beta associated with the latter is substantially higher 
in size than the former, suggesting that it has the largest long run beneficial impact. More, specifically, holding riskless interest 
rate, exchange rate and interest rate spread constant, a 1% increase in stock market index would concurrently increase nominal 
GDP by approximately 0.33%, while a 1% increase in credit to private sector would lead to approximately 0.55% increase in 
nominal GDP. However, the long run coefficients on riskless interest rate, exchange rate and interest rate spread are estimated at 
0.1074, 0.0884 and -0.0636 with associated p-values of 0.0979, 0.1537 and 0.2060), suggesting that they are relatively small 
and their effects on nominal GDP are not statistically significant at 5% level. The long run effect of riskless interest rate is 
significant at 10% level. The long run intercept term (beta = 2.0663, p-value = 0.0000) is positive and highly significant, 
suggesting that on average, Nigeria would record significant economic growth even when all included financial conditions 
indicators are kept dormant.   

Overall, our results suggest that financial conditions variables have a significant effect on economic growth in Nigeria 
both in the short run and in the long run. Also, credit to private sector and stock market index are the most significant factors 
for nominal GDP, hence have the highest weights in the construction of the financial conditions index. This evidence is, 
therefore, consistent with both credit and asset price channels of monetary policy transmission mechanism. This finding also 
agrees with the supply leading theory of finance-growth relationship as well as several previous studies including Acaravci,Ozturk 



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and Acaravci(2007), Al-Malkawi and Abdullah(2011), Ibrahim, Abdullahi, Azman-Saini and Rahman(2017) and Oro and 
Alagidede(2018), Ipeghan and Marshall(2019)and Olaniyan (2019).   
 
4.2 Financial Conditions Index and Economic Performance 
Figure 2 shows the financial conditions index for Nigeria using the ARDL coefficients as weights. As this Figure shows, the 
constructed financial conditions index showed a steady upward trend from negative values to positive values over the sampled 
period. Specifically, the negative values of the index were recorded ‘between’ 1985 to 1999, suggesting that before the advent of 
the current democracy, Nigeria’s financial conditions were on average looser than the prevailing macroeconomic conditions. On 
the contrary, the positive values recorded from 2000 to 2018 shows that Nigeria’s financial conditions gradually become tighter 
than the prevailing macroeconomic conditions in the post democracy period.  

-6

-4

-2

0

2

4

1985 1990 1995 2000 2005 2010 2015

FCI

 
 

Figure 2. Financial Conditions Index for Nigeria 
 

Table 3 shows the three variable VAR(1) results for the dynamic relationships between financial conditions index, 
inflation and real GDP growth rate. The p-values are obtained from the system equations estimated via OLS method. Figure 
3shows the VAR inverted roots with respect to unit circle. Figures 4 and 5 show the impulse response function and variance 
decomposition of real GDP growth rate, inflation and financial conditions index. 

From Table 3, we can see that both the autocorrelation (p-value = 0.2646) and heteroskedasticity (p-value = 0.4620) 
test statistics are associated with a probability, hence not rejecting the null hypotheses of no serial correlation and 
heteroskedasticity. The inverted VAR roots in Figure 3 are all inside the unit circle which shows that the estimated VAR model 
is stable and can be used for structural analysis.  

From Figure 4, we can see that while real GDP growth rate responded positively to a one standard deviation shocks in 
financial conditions index, inflation rate responded negatively. However, for both real economic variables, the impact of financial 
conditions index is marginal throughout the impact period, hence, lacks economic significance. The forecast error variance 
decomposition of real GDP growth in Figure 5 shows that financial conditions index contributed less than 0.02% of the 
variance of real GDP growth throughout the forecast period, while it contributed approximately 0.07% of the variance of 
inflation rate in the fourth period. These results imply that financial conditions index has a little or no predictive power for both 
economic growth and inflation in Nigeria. Our results, therefore, contradict the recent findings of Wang, Xu and Chen (2018) 
and Kabundi and Mbelu (2020) for China and South Africa respectively. We argue that the method of constructing the 
financial conditions index can plausibly explain these differing results.  
 

  Table 3. VAR Results for FCI and Economic Performance; p-value in ( ) 
 

Variable  RGDPG LINFL FCI 

GDPG(-1) 0.5053 (0.0222) -0.0220 (0.6469) -0.0060 (0.4396) 

LINFL(-1) 0.4962 (0.3650) 0.1566 (0.3384) 0.0170 (0.5220) 



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FCI(-1) 0.1597 (0.4673) -0.0894 (0.1745) 0.9602 (0.0000) 

Constant 1.0476 (0.5646) 2.3245 (0.0000) 0.2493 (0.0059) 

LM (2) statistic 11.164 (0.2646) 

Heteroskedasticity Test 36.143 (0.4620) 

  

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

 
 

Figure 3. Inverted AR Characteristic Roots  
 

-1

0

1

2

3

4

5

1 2 3 4

Response of GDPG to GDPG

-1

0

1

2

3

4

5

1 2 3 4

Response of GDPG to LINFL

-1

0

1

2

3

4

5

1 2 3 4

Response of GDPG to FCI

-0.5

0.0

0.5

1.0

1.5

1 2 3 4

Response of LINFL to GDPG

-0.5

0.0

0.5

1.0

1.5

1 2 3 4

Response of LINFL to LINFL

-0.5

0.0

0.5

1.0

1.5

1 2 3 4

Response of LINFL to FCI

-.2

-.1

.0

.1

.2

.3

1 2 3 4

Response of FCI to GDPG

-.2

-.1

.0

.1

.2

.3

1 2 3 4

Response of FCI to LINFL

-.2

-.1

.0

.1

.2

.3

1 2 3 4

Response of FCI to FCI

Response to Cholesky One S.D. Innovations ± 2 S.E.

 
 

Figure 4. Impulse Response Function 



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-40

0

40

80

120

1 2 3 4

Percent GDPG variance due to GDPG

-40

0

40

80

120

1 2 3 4

Percent GDPG variance due to LINFL

-40

0

40

80

120

1 2 3 4

Percent GDPG variance due to FCI

-40

0

40

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120

1 2 3 4

Percent LINFL variance due to GDPG

-40

0

40

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120

1 2 3 4

Percent LINFL var iance due to LINFL

-40

0

40

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120

1 2 3 4

Percent LINFL var iance due to FCI

-40

0

40

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120

1 2 3 4

Percent FCI var iance due to GDPG

-40

0

40

80

120

1 2 3 4

Percent FCI var iance due to LINFL

-40

0

40

80

120

1 2 3 4

Percent FCI var iance due to FCI

Variance Decomposition ± 2 S.E.

 
 

Figure 5. Forecast Error Variance Decomposition 
 

5. Conclusion 
In this paper, we constructed a financial conditions index for Nigeria and examine its predicting power for both economic 
growth and inflation rate using yearly time series data from 1985 to 2018. The variables included in the construction of the 
index are riskless interest rate, stock market index, exchange rate, credit to private sector and interest rate spread. While the 
weights attached to these variables are ARDL coefficients, the predictive power of the constructed index is examined within the 
VAR framework. We conclude as follows: 

First, the results from the ARDL model shows that credit to private sector and stock market index are the most 
significant factors for nominal GDP, hence having a substantial weight in the resultant financial conditions index. However, the 
results from VAR impulse response function and forecast error variance decomposition suggest that the constructed financial 
conditions index contain very little information about future growth rate and inflationary trend.  
 
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