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Testing Fiscal Solvency in Macroeconomics

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Decision Economics: Complexity of Decisions and Decisions for Complexity (DECON 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1009))

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Abstract

This paper presents a literature review regarding the assessment of government solvency and fiscal policy. While indicators for fiscal sustainability are forward looking and based on expected future fiscal policies, tests are backward looking and based on information about the past values of fiscal variables. In this paper, we describe the main tests for fiscal solvency.

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Notes

  1. 1.

    Eq. (6) should be specified either in levels or in first differences, according to what is necessary to achieve stationarity.

  2. 2.

    When the surplus equation is specified in differences, it is also convenient to specify the debt equation in the same way.

  3. 3.

    The exact form of the non-linear constraints can be found in West (1987).

  4. 4.

    The main advantages of this sustainability test are that it does not require any assumptions on interest rates and it maintains its validity under conditions of uncertainty.

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Authors and Affiliations

  1. Luiss University, Rome, Italy

    Paolo Canofari

  2. University of Tor Vergata, Rome, Italy

    Alessandro Piergallini

Authors
  1. Paolo Canofari
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  2. Alessandro Piergallini
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Correspondence to Paolo Canofari .

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Editors and Affiliations

  1. Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Italy

    Edgardo Bucciarelli

  2. AI-ECON Research Center - Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  3. Edificio Parque Científico Universidad de Valladolid, AIR Institute, Salamanca, Spain

    Juan Manuel Corchado

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Canofari, P., Piergallini, A. (2020). Testing Fiscal Solvency in Macroeconomics. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics: Complexity of Decisions and Decisions for Complexity. DECON 2019. Advances in Intelligent Systems and Computing, vol 1009. Springer, Cham. https://doi.org/10.1007/978-3-030-38227-8_9

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