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Government tax revenue, expenditure, and debt in sri lanka: A vector autoregressive model analysis

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dc.contributor.author Uthayakumar, S.S.
dc.contributor.author Selvamalai, T.
dc.date.accessioned 2022-01-21T06:03:32Z
dc.date.accessioned 2022-06-29T07:16:44Z
dc.date.available 2022-01-21T06:03:32Z
dc.date.available 2022-06-29T07:16:44Z
dc.date.issued 2016
dc.identifier.issn 2279-2406
dc.identifier.uri http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/5140
dc.description.abstract The study has applied Vector Autoregressive (VAR) Model of times series econometric techniques to examine the relationship between government tax revenue, expenditure and debt in Sri Lanka from 1950 to 2015. The data were gathered from Annual Report of Central Bank of Sri Lanka, 2015. The study was found several interesting results. Results of variance decomposition analysis concluded that impact of the external shock on forecast error in tax can be negligible but it can be negligible in the case of government expenditure and debt. Further, the impact of the own shock on forecast error in all cases cannot be negligible. The results of impulse response function concluded that responses of the system to standard deviation shock in a single variable were meaning full only in the short-run (up to five periods). The results of Granger Causality test concluded that government tax revenue did Grange cause government expenditure and debt in Sri Lanka not vice versa at 5% significant level. And there was a uni-directional causal relation from government expenditure to debt at 5% significant level. Further, at 10% significant level, there was a bi-directional Granger Causality between government tax revenue and debt in Sri Lanka. en_US
dc.language.iso en en_US
dc.publisher University of Jaffna en_US
dc.subject Tax en_US
dc.subject Expenditure en_US
dc.subject Debt en_US
dc.subject Vector autoregressive (VAR) model en_US
dc.subject Impulse response function en_US
dc.title Government tax revenue, expenditure, and debt in sri lanka: A vector autoregressive model analysis en_US
dc.type Article en_US


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