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Revenue and Expenditure Nexus: A Case Study of Romania

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  • HYE, Qazi Muhammad Adnan
  • M Anwar, Jalil

Abstract

This study determines the causal relationship between the expenditure and revenue of government in the case of Romania by using the autoregressive distributive lag approach to cointegration, variance decomposition and rolling regression method. The results indicate that bidirectional long run relationship exist between expenditure and revenue of government. The variance decomposition method suggests government revenue shock has more sharply impact on the government expenditure as compared to the shock in government expenditure and response of government revenue collection.

Suggested Citation

  • HYE, Qazi Muhammad Adnan & M Anwar, Jalil, 2010. "Revenue and Expenditure Nexus: A Case Study of Romania," MPRA Paper 32132, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:32132
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    File URL: https://mpra.ub.uni-muenchen.de/32132/1/MPRA_paper_32132.pdf
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    References listed on IDEAS

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    1. James Payne & Hassan Mohammadi & Murat Cak, 2008. "Turkish budget deficit sustainability and the revenue-expenditure nexus," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 823-830.
    2. Eita, Joel Hinaunye & Mbazima, Daisy, 2008. "The Causal Relationship Between Government Revenue and Expenditure in Namibia," MPRA Paper 9154, University Library of Munich, Germany.
    3. repec:kap:iaecre:v:15:y:2009:i:2:p:143-155 is not listed on IDEAS
    4. Paresh Kumar Narayan & Seema Narayan, 2006. "Government revenue and government expenditure nexus: evidence from developing countries," Applied Economics, Taylor & Francis Journals, vol. 38(3), pages 285-291.
    5. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    6. YAN Chengliang & GONG Liutang, 2009. "Government expenditure, taxation and long-run growth," Frontiers of Economics in China, Higher Education Press, vol. 4(4), pages 505-525, December.
    7. Shyh-Wei Chen, 2008. "Untangling the web of causalities among four disaggregate government expenditures, government revenue and output in Taiwan," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 6(1), pages 99-107.
    8. Xiaoming Li, 2001. "Government revenue, government expenditure, and temporal causality: evidence from China," Applied Economics, Taylor & Francis Journals, vol. 33(4), pages 485-497.
    9. Andreea Stoian, 2008. "Analyzing Causality Between Romania’S Public Budget Expenditures And Revenues," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 11(11(528)(s), pages 60-64, November.
    10. Matthew Zapf & James Payne, 2009. "Asymmetric modelling of the revenue-expenditure nexus: evidence from aggregate state and local government in the US," Applied Economics Letters, Taylor & Francis Journals, vol. 16(9), pages 871-876.
    11. Judith Stallmann & Steven Deller, 2010. "Impacts of local and state tax and expenditure limits on economic growth," Applied Economics Letters, Taylor & Francis Journals, vol. 17(7), pages 645-648.
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    Citations

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    Cited by:

    1. Ullah, Nazim, 2016. "The Relationship of Government Revenue and Government Expenditure: A case study of Malaysia," MPRA Paper 69123, University Library of Munich, Germany.
    2. Phiri, Andrew, 2016. "Asymmetries in the revenue-expenditure nexus: New evidence from South Africa," MPRA Paper 75224, University Library of Munich, Germany.
    3. Damian C. NWOSU & Harrison O. OKAFOR, 2014. "Government Revenue and Expenditure in Nigeria: A Disaggregated Analysis," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(7), pages 877-892, July.

    More about this item

    Keywords

    Government revenue; government expenditure; cointegration;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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