TESTING OF PARAMETER CONSTANCY AND DYNAMIC NEXUS BETWEEN GOVERNMENT REVENUE AND ITS EXPENDITURE: EMPIRICAL EVIDENCE FROM INDONESIA

Subrata Saha

Volume : V, Issue : II, February - 2015

TESTING OF PARAMETER CONSTANCY AND DYNAMIC NEXUS BETWEEN GOVERNMENT REVENUE AND ITS EXPENDITURE: EMPIRICAL EVIDENCE FROM INDONESIA

Subrata Saha

Abstract :

This study examines the direction of causality between government revenue and its expenditure in the short–run and long–run by using Vector Error Correction Model and Unrestricted Vector Autoregressive Model during entire sample period. This paper is devoted to examine the stability of parameter throughout the entire time period and its impact in the existing causal relationship between government revenue and government expenditure. The unit root test confirms non–stationarity of revenue and expenditure series during the period of study. There exists a long–run relationship between government revenue and government expenditure as evident from Engel–Grager Two step Method. The Vector Error Correction Model exhibits no causality between government revenue and its expenditure in the short–run while in the long–run, Indonesia government takes the decision of revenue first and then expenditure i.e Tax–and–Spend principle as observed from Unrestricted Vector Autoregressive Model. The dataset exhibits that a structural change has been occurred in the year 1997 as revealed from Chow test. Vector Error Correction Model confirms Bi–directional causality between government revenue and its expenditure in the short–run during the sub–period 1968–1997 while fiscal neutrality principle is observed in the long–run. The empirical findings from 1998 to 2008 reveal that uni–directional causality running from revenue to expenditure exists in the short–run but no causal (Granger) relationship between revenue and expenditure exists in the long–run memory. The estimated causal relationship with historical dataset may not provide reliable guideline for making policy decisions. On the other hand, the estimated causal relationships under different sub–periods may provide reliable guidelines for decision making process.