MODELING THE RELATIONSHIP BETWEEN MONETARY POLICY AND ECONOMIC GROWTH IN NIGERIA: AN APPLICATION OF THE ARDL APPROACH IN THE PRESENCE OF STRUCTURAL BREAKS.

Abstract

The role of monetary policy in sustaining economic growth has been a highly researched subject. The aim of this study is to examine the relationship between monetary policy and economic growth when there is evidence of structural break. Quarterly time series data was collected from the Central Bank of Nigeria Statistical Bulletin and Website from 1981 to 2021. Motivated by the prevalence of misleading inference in time series occasioned by failure to account for structural breaks in series as volatile as macroeconomic variables in Nigerian specific studies, this study sought to find out whether structural breaks matter in studying the response of Economic growth due to monetary policy shocks. The study employed Zivot-Andrews unit root test with structural break to compare the unit root result with the conventional ADF result while the Autoregressive Distributed Lag (ARDL) bounds testing approach is used to investigate the co-integration among the variables in the presence of structural breaks. The unit root test shows that failure to account for structural break in unit root of a volatile series can produce wrong inference. After allowing for structural breaks, the study finds no evidence of co-integration relationship between economic growth and monetary policy. Thus it can be argued that there exists only a short run relationship between the variables of study. The estimates of the ARDL short run model suggest that Money Supply (M2) has a significant positive impact on economic growth in the short run at the selected lag length. However, the estimates show that Net Credit to Government (NCG) has a negative significant impact on economic growth in Nigeria. More also, Exchange Rate (EXR), Inflation (INFL) and Maximum Lending Rate (MLRC) have a positive but insignificant effect on Economic Growth in the short run. This study reveals that broad money supply lead to economic growth in the short run. Structural change is persistent in macroeconomic time series data, and it can be quite hazardous to ignore as inferences about economic relationships can go off track and policy recommendations can be deceptive or worse, therefore Researchers working with macroeconomic time series data are recommended not to use only unit root tests such as ADF, DF-GLS, and PP that do not account for structural breaks, but unit root tests that account for structural change, such as the Zivot Andrews test, the Chow test, and the Bai Perron multiple structural break test, should also be employed.