Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage

Matabel Odin Odiaga; Samuel Musili Mwalili; Joseph Kyalo Mung’atu

doi:doi:10.11648/j.ijdsa.20180405.14

| Peer-Reviewed

Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage

Matabel Odin Odiaga, Samuel Musili Mwalili, Joseph Kyalo Mung’atu

Published in International Journal of Data Science and Analysis (Volume 4, Issue 5)

Received: 24 October 2018 Accepted: 8 November 2018 Published: 4 December 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.

Published in	International Journal of Data Science and Analysis (Volume 4, Issue 5)
DOI	10.11648/j.ijdsa.20180405.14
Page(s)	98-105
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Extreme Value Theory, Non-stationary GPD, Bayesian, Debt Indicators, Budget Deficit, Budgetary Leakage

References

[1]	Ngunjiri, N., "What level of public debt is desirable for Kenya?," 26 February 2018. [Online]. Available: https://www.businessdailyafrica.com/analysis/letters/What-level-of-public-debt-is-desirable-for-Kenya-/4307714-4320606-1axwhxz/inde. [Accessed 2018 April 2018].
[2]	M. W. Nandelenga, "Debt sustainability and the optimal debt in Kenya," 2010.
[3]	C. K. Kiptoo, "The determinants of Kenya extrenal debt sustainability," Doctoral dissertation, school of Business, University of Nairobi, 2012.
[4]	C. M. Matiti, "The effect of selected determinants on public debt in Kenya," 2013.
[5]	J. W. Mathenge, "Determine the breaking point of Kenya debt an application of extreme value theory," 2017.
[6]	L. Ochieng', "Kenya loses over Sh600bn every year in tax evasion," Nation Media Group, 21 May 2015. [Online]. Available: https://www.nation.co.ke/business/Kenya-loses-over-Sh600bn-every-year-in-tax-evasion/996-2724818-5jswbtz/index.html. [Accessed 25 April 2018].
[7]	E. Smith, "Bayesian modeling of extreme rainfal data," Doctoral dissertation, Univeristy of Newcastle upon Tyne, 2005.
[8]	F. De Paola, M. Giugni, F. Pugliese, A. Annis and F. Nardi., "GEV Parameter estimation and stationary vs. non-stationary analysis of extreme rainfall in african test cities," Hydrology, vol. 5, no. 2, p. 28, 2018.
[9]	L. A. A. Cheng Linyin, E. Gilleland and R. W. Katz, "Non-stationary extreme value analysis in a changing climate.," Climatic change, vol. 127, no. 2, pp. 353-369.
[10]	P. Jonathan, D. Randell, Y. Wu and K. Ewans, "Return level estimation from non-stationary spatial data exhibiting multidimensional covariate effects," Ocean Engineering, vol. 88, pp. 520-532, 2014.
[11]	A. C. Davison and R. L. Smith, "Models for exceedances over high thresholds," Journal of the Royal Statistical Society. Series B (Methodological), pp. 393-442, 1990.
[12]	J. Tawn, "An extreme-value theory model for dependent observations.," Journal of Hydrology, vol. 101, no. 1-4, pp. 227-250, 1988.
[13]	R. A. Fisher and L. H. C. Tippett, "Limiting forms of the frequency distribution of the largest or smallest member of a sample," Mathematical Proceedings of the Cambridge Philosophical Society, vol. 24, no. 2, pp. 180-190, 1928.
[14]	L. De Haan and A. Ferreira, Extreme value theory: an introduction, New York: Springer Science & Business Media, 2007.
[15]	P. Northrop, N. Attalides and P. Jonathan, "Cross‐validatory extreme value threshold selection and uncertainty with application to ocean storm severity.," Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 66, no. 1, pp. 93-120, 2017.

Cite This Article

Plain Text BibTeX RIS

APA Style

Matabel Odin Odiaga, Samuel Musili Mwalili, Joseph Kyalo Mung’atu. (2018). Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. International Journal of Data Science and Analysis, 4(5), 98-105. https://doi.org/10.11648/j.ijdsa.20180405.14

Copy | Download

ACS Style

Matabel Odin Odiaga; Samuel Musili Mwalili; Joseph Kyalo Mung’atu. Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. Int. J. Data Sci. Anal. 2018, 4(5), 98-105. doi: 10.11648/j.ijdsa.20180405.14

Copy | Download

AMA Style

Matabel Odin Odiaga, Samuel Musili Mwalili, Joseph Kyalo Mung’atu. Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. Int J Data Sci Anal. 2018;4(5):98-105. doi: 10.11648/j.ijdsa.20180405.14

Copy | Download

@article{10.11648/j.ijdsa.20180405.14,
  author = {Matabel Odin Odiaga and Samuel Musili Mwalili and Joseph Kyalo Mung’atu},
  title = {Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage},
  journal = {International Journal of Data Science and Analysis},
  volume = {4},
  number = {5},
  pages = {98-105},
  doi = {10.11648/j.ijdsa.20180405.14},
  url = {https://doi.org/10.11648/j.ijdsa.20180405.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180405.14},
  abstract = {Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage
AU  - Matabel Odin Odiaga
AU  - Samuel Musili Mwalili
AU  - Joseph Kyalo Mung’atu
Y1  - 2018/12/04
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180405.14
DO  - 10.11648/j.ijdsa.20180405.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 98
EP  - 105
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20180405.14
AB  - Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.
VL  - 4
IS  - 5
ER  -

Copy | Download

Author Information

Matabel Odin Odiaga

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Samuel Musili Mwalili

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph Kyalo Mung’atu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Matabel Odin Odiaga, Samuel Musili Mwalili, Joseph Kyalo Mung’atu. (2018). Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. International Journal of Data Science and Analysis, 4(5), 98-105. https://doi.org/10.11648/j.ijdsa.20180405.14

Copy | Download

ACS Style

Matabel Odin Odiaga; Samuel Musili Mwalili; Joseph Kyalo Mung’atu. Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. Int. J. Data Sci. Anal. 2018, 4(5), 98-105. doi: 10.11648/j.ijdsa.20180405.14

Copy | Download

AMA Style

Matabel Odin Odiaga, Samuel Musili Mwalili, Joseph Kyalo Mung’atu. Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage. Int J Data Sci Anal. 2018;4(5):98-105. doi: 10.11648/j.ijdsa.20180405.14

Copy | Download

@article{10.11648/j.ijdsa.20180405.14,
  author = {Matabel Odin Odiaga and Samuel Musili Mwalili and Joseph Kyalo Mung’atu},
  title = {Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage},
  journal = {International Journal of Data Science and Analysis},
  volume = {4},
  number = {5},
  pages = {98-105},
  doi = {10.11648/j.ijdsa.20180405.14},
  url = {https://doi.org/10.11648/j.ijdsa.20180405.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180405.14},
  abstract = {Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage
AU  - Matabel Odin Odiaga
AU  - Samuel Musili Mwalili
AU  - Joseph Kyalo Mung’atu
Y1  - 2018/12/04
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180405.14
DO  - 10.11648/j.ijdsa.20180405.14
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 98
EP  - 105
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20180405.14
AB  - Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.
VL  - 4
IS  - 5
ER  -

Copy | Download