Abstract:
Nowadays, in Sri Lanka, the emergent public debt and its servicing costs are an unadorned
burden on the economy. The main aim of the present study is to develop a model which reflects
the relationship between public debt and economic growth in Sri Lanka using Non-Linear Auto
Regressive Distributed Lag model. Economic growth was reflected by the annual GDP growth.
Data were acquired from Department of Census and Statistics abstract reports and annual
reports of Central Bank of Sri Lanka. As the first step data were analyzed to invent that
relationship between Public debt and annual GDP growth is linear, using Auto Regressive
Distributed Lag model and it was confirmed that there was no any significant linear
relationship among variables (GDP growth and Public Debt). Then Non-linear Auto
Regressive Distributed Lag model was fitted using GDP growth and Public Debt as variables.
The Bound’s test and Wald’s test indicated the presence co-integration among variables GDP
growth and Public Debt. The estimated Auto Regressive Distributed Lag model affirms the
presence of asymmetries in GDP Growth behavior in long run. In the short run, it can be
concluded that, if one-point positive change of fourth lag in Gross Total Public Debt will lead
to 1.17 increase in GDP Growth and one-point increase in first and third lags of first difference
of Real GDP Growth will lead to 1.07 and 0.26 increase in GDP Growth when all the other
variables are constant. Furthermore, in the long run, one-point positive change of first lag in
Gross Total Public Debt will leads to 0.35 decrease in GDP Growth while one-point negative
change in first lag in Gross Total Public Debt will lead to 1.1 increase in GDP Growth when
all the other variables are constant. All the changes reflected significant influence on the GDP
Growth behavior. The both dynamic and static forecast values estimated from the developed
Non-Linear ARDL model for the period during 1970 to 2017 were almost the same with
actuals. However, the dynamic forecasting is more superior than the static forecast. The errors
from both dynamic and static models were found to be random.
