This paper uses linear and nonlinear panel causality tests to empirically explore the direction of causality between external debt stocks and credit ratings for a group of developing countries over the period 1998 to 2008.  The results indicate that for the vast majority of the countries in the panel, a bi-directional causal relationship between external debt and sovereign ratings is evident

Craigwell, Roland

Drakes, Lisa

Greenidge, Kevin

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
A Note on Causality between Debt and
Sovereign Credit Ratings using Panel
Tests
Kevin Greenidge and Lisa Drakes and Roland Craigwell
Research Department, Central Bank of Barbados, Barbados,
Department of Economics, University of the West Indies, Cave Hill
Campus, Barbados
2011
Online at https://mpra.ub.uni-muenchen.de/40931/
MPRA Paper No. 40931, posted 29. August 2012 04:31 UTC
1 
A Note on Causality between Debt and Sovereign Credit 
Ratings using Panel Tests 
 
By 
Kevin Greenidge 
Lisa Drakes 
Research Department 
Central Bank of Barbados 
BARBADOS 
kevin.greenidge@centralbank.org.bb 
lisa.drakes@centralbank.org.bb 
 
And 
 
Roland Craigwell 
Department of Economics 
University of West Indies 
Cave Hill Campus 
BARBADOS 
roland.craigwell@cavehill.uwi.edu 
 
Abstract 
This paper uses linear and nonlinear panel causality tests to empirically 
explore the direction of causality between external debt stocks and credit 
ratings for a group of developing countries over the period 1998 to 2008.  
The results indicate that for the vast majority of the countries in the panel, a 
bi-directional causal relationship between external debt and sovereign 
ratings is evident. 
 
Keywords: External public debt, sovereign ratings, panel data causality  
JEL Classification: C23: H63  
2 
1. Introduction 
Sovereign credit ratings are the risk assessments assigned by credit rating 
agencies to the obligations of central governments and are often used to 
determine whether or not loans are given, and the terms and conditions 
attached. These ratings therefore have implications for interest costs and, by 
extension, the fiscal deficit and future debt levels.  Given the importance of 
sovereign ratings, several studies have sought to identify their determinants 
(see the survey of Alfonso et al., 2007). This literature has revealed that 
debt, inter alia, is an important right-hand side variable that is posited to be 
exogenous. This paper for the first time relaxes the exogeneity assumption 
by examining the causal relationship between debt and sovereign ratings in 
developing countries. It also acknowledges that a nonlinear causal link may 
exist between these two variables, given the ordinal nature of ratings as 
described by Bissoondoyal-Bheenick (2005). Therefore, linear and 
nonlinear causality methods are conducted on a 1998 to 2008 panel data set 
for 32 developing countries, to define the association between debt and 
sovereign credit ratings. The panel causality methods are described in the 
next section. Then, the results of the empirical analysis are discussed in the 
third section. This is followed by some concluding remarks. 
 
3 
2. Methodology and Data  
2.1. Panel Causality Linear Tests 
There are basically two approaches to examining causality within a panel 
framework. The first, developed by Holtz-Eakin et al. (1988) allows the 
autoregressive and regression coefficients of the panel to vary. This reduces 
significantly the degrees of freedom and relies on the ‘large time 
dimension’ assumption to derive consistent estimates. The second, 
suggested by Hurlin and Venet (2001) and Hurlin (2004) treats these 
coefficients as constant and is perhaps more appropriate for the current data 
set. The procedure, which is detailed in Hurlin (2004), is summarized 
below. 
Consider the following time-stationary bi-variate vector auto-regression 
representation in panel form for N countries over T time periods:  
, , , , , ,
1 1
p p
i t i i k i t k i k i t k i t
k k
y y x    
 
      (1) 
where the individual effects i ,  the autoregressive coefficients βk and the 
regression coefficients Φk’s are constant for all cross-section units i and lag 
orders, k є [1, N]. First, homogenous and instantaneous non-causality 
(HINC) is checked by undertaking the following Wald test under the null 
that Φk=0 for all i and k: 
4 
 
( ) /
/ (1 )
r u
HINC
u
SSR SSR Np
F
SSR NT N p p


  
 (2)
 
where SSRu is the sum of squared residuals from Equation (1) and SSRr is 
the restricted sum of squared residuals under H0= Φk = 0 for all i and k.  If it 
is not significant (note Hurlin (2004) provides the exact critical values when 
T is small), the HINC hypothesis is accepted. This result implies that the 
variable x is not causing y in all the countries of the sample. Hence, the non-
causality result is then totally homogenous and the testing procedure goes 
no further.  
If the HINC is rejected then two possibilities exist. The first is that there is a 
causal relationship between the two variables for each country and that this 
relationship is identical for all countries in the sample. This is termed 
homogenous causality (HC) and is very unlikely to occur. The more 
plausible hypothesis is that the causal relationships differ across countries. 
This is referred to as heterogeneous non-causality (HENC) and is the test of 
   0 ,: 0 1, , 0,i kH i N k p       against 1 ,: 0 [1, ], [0, ]i kH i N k p       
with 
 
"( ) / ]
/ (1 2 )
r u
HC
u
SSR SSR p
F
SSR NT N p p


  
. 
2.2. Panel Causality Non-linear Tests 
Non-linear causality tests were first introduced by Baek and Brock (1992) 
using nonparametric methods of spatial probabilities.  Harvey and 
5 
Leybourne (2007) criticized these tests on the grounds that they failed to 
provide appropriate statistics and suggested using the following regression 
model to test that EDEBT causes FXLT : 
3
16
2
15
14
3
33
2
22110
)()( 



itit
itititit
EDEBTEDEBT
EDEBTEDEBTEDEBTEDEBTFXLT


(3)
 
A similar expression can be derived for FXLT  causes EDEBT by 
interchanging EDEBT and FXLT in Equation (3).  The same steps that were 
undertaken with the Hurlin (2004) linear panel causality approach can then 
be followed. 
2.3. Data 
To arrive at a consistent data set, an initial group of countries was reduced 
to 32 developing countries and estimated over the annual period 1998 to 
2008. Long- and short-term, foreign currency and local currency ratings 
were sourced from Standard and Poor’s (S&P) website. The external debt 
data, expressed as a percentage of exports of goods, services and income 
and the fiscal balance, given as the cash surplus/deficit as a percentage of 
GDP were obtained from the World Bank’s World Development Indicators. 
The other variables – GDP growth and inflation (percentage change in the 
consumer price index) – were extracted from the IMF’s International 
Financial Statistics database. 
3. Empirical Results  
Panel unit tests indicated that all the variables are stationary in levels 
(results available on request). Hence the panel Granger causality methods 
6 
can be conducted on the statistical significance of the regression coefficients 
using the above mentioned Wald statistics.  
3.1. Linear Panel Causality Results 
Given that the variables were stationary in levels the panel regression 
equations were estimated in levels using the pooled ordinary least square 
(OLS) model and the fixed effects (LSDV) model. The pooled model 
assumes that the intercept (α) and slope coefficients (γ and β) do not vary 
across countries, while LSDV allows for a changing α.  In general, results 
of the HINC test (see Table 1) across the estimation approaches employed 
and the lag lengths all suggest that the null of no homogenous and 
instantaneous causality between external debt and sovereign ratings, or 
from sovereign ratings to external debt cannot be accepted at conventional 
significant levels.  In other words, there appears to be a bi-directional causal 
relationship between external debt and sovereign ratings. Table 1 also 
shows that these results are robust to the inclusion of controls variables that 
capture the effects of the fiscal balance, per capita income, inflation and 
GDP growth rate on external debt and sovereign ratings. 
Given that there is evidence of causality between these two variables, the 
authors then investigate whether the causality is sourced from 
heterogeneous causal relationships for each country (see Tables 2).  The 
HENC results (Table 2) show that there is a bi-directional relationship 
between sovereign ratings and external debt in 26 of the 32 countries 
7 
studied; the other 6 - Bolivia, Brazil, Indonesia, Pakistan, Peru, and 
Uruquay - reveal no causality between external debt and sovereign ratings. 
Table 1: Homogenous and Instantaneous Non-Causality Tests  
(No Controls and Controls) 
   
HINC 
(No 
Controls) 
  
HINC 
(With 
Controls) 
 
 Lags OLS – 
Levels 
Fixed 
effects – 
Levels 
OLS – 
Levels 
Fixed 
effects – 
Levels 
FXLTEDEBT  1 22.31*** -7.08*** 21.75*** -7.14*** 
 2 21.06*** -7.28*** 20.55*** -7.36*** 
 3 19.96*** -5.26*** 19.52*** -5.00*** 
FXSTEDEBT  1 23.94*** -3.64*** 23.37*** -3.74*** 
 2 22.61*** -3.68*** 22.13*** -3.70*** 
 3 21.27*** -2.80*** 20.89*** -2.58** 
EDEBTFXLT  1 23.01*** -3.38*** 22.45*** -3.57*** 
 2 22.85*** 0.019 22.33*** -0.05 
 3 22.37*** 3.23*** 21.85*** 4.36*** 
EDEBTFXST  1 24.26*** -1.69* 23.68*** -2.01** 
 2 24.02*** 0.422 23.42*** 0.35 
 3 23.23*** 1.69* 22.61*** 2.45** 
Note: ***,** and * indicates significance at the 1,5 and 10 percent level, 
respectively. 
8 
Table 2: Heterogeneous Granger Linear Causality Tests 
Count
ry 
FXLTEDEBT
 
FXSTEDEBT
 
EDEBTFXLT
 
EDEBTFXST
 ARG -2.85*** -1.93* 4.90*** 4.45*** 
BGR 4.31*** 4.15*** -2.66*** -2.24** 
BOL -1.51 -1.09 0.29 0.93 
BRA -0.92 -0.61 -0.28 0.24 
CHL 5.66*** 6.98*** -4.01*** -3.46*** 
COL 1.72* 1.53 -2.16** 0.94 
CRI 6.69*** 6.37*** -4.22*** -3.24*** 
DOM 4.56*** 6.17*** -3.20*** -2.90*** 
EGY 3.81*** 3.27*** -3.79*** -.2.66*** 
IDN -0.03 0.78 -0.39 -0.38 
IND 4.05*** 3.84*** -3.52*** -2.54** 
JAM 2.59** 3.91*** -1.69* -1.03 
JOR 3.68*** 3.66*** -3.37*** -2.40** 
KAZ 3.91*** 3.89*** -1.80* -1.21 
LTU 8.12*** 9.14*** -3.91*** -3.29*** 
LVA 3.55*** 3.86*** -1.84* -1.01 
MAR 3.50*** 3.08*** -3.67*** -2.67*** 
MEX 7.0*** 7.08*** -4.04*** -3.25*** 
MYS 11.25*** 11.45*** -5.23*** -4.43*** 
PAK -0.69 0.25 0.51 -0.14 
PER -0.26 -0.14 -1.31 -0.33 
PHL 3.78*** 3.64*** -3.31*** -2.056** 
POL 6.25*** 6.88*** -3.79*** -3.10*** 
PRY 2.11** 2.93*** -2.50** -1.90* 
ROM 4.72*** 4.85*** -2.05** -1.76* 
RUS 4.18*** 4.47*** -2.31** -2.44** 
SLV 5.17*** 4.36*** -3.25*** -2.00** 
THA 7.19*** 7.93*** -4.85*** -4.13*** 
TTO 1.75* 2.16** -1.71* -1.14 
TUN 5.16*** 4.89*** -3.43*** -2.46** 
URY 0.14 0.64 0.13 0.53 
ZAF 9.06*** 9.44*** -4.71*** -3.91*** 
9 
Note: ***,**  and * indicates significance at the 1,5 and 10 percent level of 
testing, respectively. 
 
3.2. Non Linear Panel Causality Results 
Non-linear Granger causality between debt and credit ratings can be tested 
using Equation (3) and its variant. The results (see Table 3) confirm the 
findings of the linear causality methods that there is a bi-directional 
relationship between these two variables.  Using the HENC tests bi-
directional causality is evident for fifteen countries, namely: Bulgaria, 
Chile, Costa Rica, Dominican Republic, Indonesia
1
, Lithuania, Mexico, 
Malaysia, Poland, Romania, Russia, El Salvador
3
, Thailand, Tunisia and 
South Africa (see Table 4). The tests further confirm that short and long-
term foreign currency ratings are instrumental in the determination of the 
external debt level of all the countries in the sample, with the exception of 
Argentina. 
 
                                                          
1
 The results also show that for El Salvador and Indonesia, there is only a uni-directional 
relationship from debt to long-term foreign currency rating, and there is no such link from 
debt to the short-term rating. 
10 
Table 3A: Non-Linear Causality Results: Dependent Variable (FXLT 
and FXST) 
  FXLT  FXST  
Causal 
Variable 
Lags Coefficient T-Statistic Coefficient T-Statistic 
Edebt 1 0.16 31.37*** 0.06 30.08*** 
Edebt
2
 2 0.00 -11.70*** 0.00 -10.61*** 
Edebt
3
 3 0.00 0.21 0.00 0.03 
Ln(Edebt) 1 -0.13 -7.49*** -0.04 -6.79*** 
Ln((Edebt)
2
 1 0.00 -2.62*** 0.00 -2.57** 
Ln(Edebt)
3
 1 0.00 -2.61*** 0.00 -2.68** 
 
Table 3B: Non-Linear Causality Results: Dependent Variable (EDEBT)  
Causal Variable Lags Coefficient T-Statistic 
FXLT 1 31.01 11.52*** 
FXLT
2
 2 -2.30 -6.81*** 
FXLT
3
 3 0.05 3.96*** 
Ln(FXLT) 1 -36.12 -5.30*** 
Ln(FXLT)
2
 1 2.20 2.97*** 
Ln(FXLT)
3
 1 -0.02            -0.14 
 
11 
Table 3C: Non-Linear Causality Results: Dependent Variable 
(EDEBT)  
Causal Variable Lags Coefficient T-Statistic 
FXST 1 69.06 11.61*** 
FXST
2
 2 -11.40 -5.97*** 
FXST
3
 3 0.51 2.70*** 
Ln(FXST) 1 -109.50 -8.10*** 
Ln(FXST)
2
 1 5.88              1.44 
Ln(FXST)
3
 1 -0.17             -0.23 
Note: ***,** and * indicates significance at the 1,5 and 10 percent level of 
testing, respectively. 
 
12 
Table 4: Heterogeneous Granger Non-Linear Causality Tests 
Countr
y 
FXLTEDEBT
 
FXSTEDEBT
 
EDEBTFXLT
 
EDEBTFXST
 ARG -0.01 0.00 5.24 12.66
BGR 0.03* 0.01* -11.11*** -36.44*** 
BOL -0.01 0.00 -7.08* -23.77** 
BRA 0.00 0.00 -6.41** -23.62* 
CHL 0.05*** 0.02*** -11.62*** -30.00*** 
COL -0.01 0.00 -11.26*** -32.79*** 
CRI 0.09*** 0.03*** -17.96*** -52.99*** 
DOM 0.04*** 0.03*** -20.30*** -50.71*** 
EGY 0.02 0.00 -15.11*** -49.23*** 
IDN -0.03** 0.00 -9.55*** -30.27*** 
IND 0.03 0.01 -14.85*** -43.75*** 
JAM -0.01 0.00 -13.79*** -40.34*** 
JOR 0.01 0.01 -15.62*** -48.27*** 
KAZ 0.01 0.00 -8.56*** -26.89*** 
LTU 0.09*** 0.04*** -11.83*** -30.60*** 
LVA 0.02 0.01** -5.96** -14.97* 
MAR 0.02 0.00 -15.52*** -49.09*** 
MEX 0.08*** 0.03*** -14.35*** -40.62*** 
MYS 0.34*** 0.12*** -15.83*** -44.66*** 
PAK -0.02 0.00 -6.99* -28.41*** 
PER 0.00 0.00 -8.52** -28.28*** 
PHL 0.01 0.00 -15.14*** -42.61*** 
POL 0.05*** 0.02*** -11.99*** -33.63*** 
PRY -0.02 0.00 -20.13*** -46.30*** 
ROM 0.02* 0.01** -10.85*** -33.63*** 
RUS 0.03* 0.01** -11.12*** -35.06*** 
SLV 0.03** 0.01 -13.44*** -40.68*** 
THA 0.13*** 0.05*** -15.88*** -43.64*** 
TTO 0.01 0.01 -5.70** -19.22* 
TUN 0.03** 0.01** -11.97*** -36.09*** 
URY -0.01 0.00 -6.91** -18.35* 
ZAF 0.17*** 0.07*** -15.56*** -43.714*** 
13 
Note: ***,** and * indicates significance at the 1,5 and 10 percent level of 
testing, respectively. 
 
4. Conclusion 
This paper investigates the causal relationship between sovereign ratings 
and external debt for 32 developing countries over the period 1998-2008. 
The findings from the linear and non-linear panel causality analysis show 
that there is a bi-directional causal relationship between sovereign ratings 
and external debt in several of the countries studied even after adjustments 
are made for the effects of per capita income, inflation and GDP growth 
rate. One implication of this evidence is that the downgrading of a country’s 
sovereign rating, particularly in tough times when a country may need to 
secure additional debt, can actually exacerbate the debt problem. Not only 
will the cost of hedging against losses on the country’s debt rise but the 
downgrade means some institutional investors will no longer be allowed to 
buy the country’s debt under the terms of their investment mandate and 
could lead to still higher borrowing costs. 
 
 
14 
References 
Afonso, A., P. Gomes, and P. Rother, 2007, What ‘Hides’ Behind 
Sovereign Debt Ratings? Working Paper 711, European Central Bank. 
Baek, E. and W. Brock, 1992, A General Test for Non-Linear Granger 
Causality: Bivariate Models, Working Paper, Iowa State University and 
University of Wisconsin, Madison.  
Bissoondoyal-Bheenick, E., 2005, An Analysis of The Determinants of 
Sovereign Ratings, Global Finance Journal, 15(3), 251-280. 
Harvey. D and S. Leybourne, 2007, Testing for Time Series Linearity, 
Econometrics Journal, 10: 149-165. 
Holtz-Eakin, D., W. Newey and  H.S. Rosen, 1988, Estimating Vector 
Autoregressions with Panel Data, Econometrica, 56(6), 1371-1395. 
Hurlin, C. , 2004, Testing Granger Causality in Heterogeneous Panel Data 
Models with Fixed Coefficients. Document de recherché LEO, Universite 
d’ Orleans. 
Hurlin, C. and B. Venet, 2001. Granger Causality Tests in Panel Data 
Models with Fixed Coefficients, mimeo, University Paris IX. 
Standard and Poor’s, 2008. Sovereign Credit Ratings: A Primer. 


A Note on Causality between Debt and Sovereign Credit Ratings using Panel Tests

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A Note on Causality between Debt and Sovereign Credit Ratings using Panel Tests

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