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GRA · Public Finance and Sovereign Debt

Spatial financeWhere capital meets hazard, exposure and transition

Econometrics for public finance and sovereign debt

Econometrics estimates relationships from fiscal and macroeconomic data and tests whether they hold. On this platform it supports estimates of revenue volatility, fiscal reaction functions, efficiency gaps and the effects of shocks on public debt.

02بەکارهێنان

What econometrics does here.

Econometrics applies statistical methods to economic data to estimate relationships, test theories and forecast. It provides empirical evidence on how shocks and policies affect economic outcomes. In sovereign and public finance, development finance and disaster and crisis, econometric evidence estimates how disasters, pandemics and financial shocks affect output, employment and prices. It informs the economic case for risk reduction. The canonical description is on the STEM Commons.

Fiscal reaction functions

Estimates of how governments adjust primary balances in response to debt levels inform judgements about fiscal space and credibility.

Revenue volatility

Commodity and tax revenue series are modelled to estimate volatility and its relationship with prices and output, informing buffer sizing.

Disaster effects on public finance

Panel data on past disasters estimate average effects on revenue, spending and debt, with heterogeneity across countries reported.

03چاودێری

Econometric model.

The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.

Econometric model

Public finance and sovereign debt · Econometrics for public finance · Run R-5003

Estimates stableGRA · Public Finance and Sovereign Debt

Estimates

robust standard errors

Public debt0.030.19[−0.4, 0.2]بەڵێ
Gross financing needs0.290.12[−0.2, 0.4]بەڵێ
Interest to revenue0.200.13[0.0, 0.5]نەخێر
Share of FX debt0.330.05[0.1, 0.9]بەڵێ

Method card

ڕێگا
ئابووریپێوی
Sample
1642 obs
دیزاین
panel, fixed effects
ناونیشان
مۆدێلکراو

Residuals by group

Econometric model: the screen’s structure, terms and states for Public finance and sovereign debt.ناو و بەهای نموونەیی؛ دیزاینێکە، نەک تۆمار یان سیستەمی ڕاستەوخۆ.
04داتا

What the method consumes.

Datasets and records used by econometrics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

داتاڕۆڵناونیشان
Fiscal time seriesEstimationئامارە بڵاوکراوەکان
Commodity price seriesRevenue driversچاودێریکراو
Disaster databasesEvent dataREPORTED
05Scenario model

Sovereign debt paths and the odds of breaching a threshold.

The distribution of debt paths, and the share above a threshold of concern at the horizon, that sovereign investors and rating analysts read from a fan chart. Teaching model with stated illustrative values, not a prediction.

  • Median, 25% to 75% and 10% to 90% bands
  • No shocks

کۆنترۆڵەکان

Debt today
70% of GDP
Effective nominal interest rate
5.0%
Nominal GDP growth
4.5%
Primary balance
−0.5% of GDP
Volatility of growth
2.0 points
Volatility of the interest rate
1.0 points
ساڵەکانی داهاتوو
10 years
Threshold of concern
90% of GDP

خوێندنەوەکان

Median debt at the horizon
79% of GDP
Paths above the threshold at the horizon
3%
Primary balance that stabilises debt
0.3% of GDP
ژمارە، ڕێگا و سەرچاوەکان
ساڵ10%25%Median75%90%
070.070.070.070.070.0
168.869.770.972.073.1
268.670.171.573.174.6
368.970.772.574.376.1
469.271.373.375.577.5
569.471.874.576.779.2
669.972.775.377.780.8
770.673.176.279.081.9
871.274.077.180.283.3
971.974.877.881.284.4
1072.075.278.782.085.7

ڕێگاکان

  1. Debt dynamics and the interest-growth differential Blanchard (2019), Public debt and low interest rates, American Economic Review 109
  2. Stochastic fan charts Celasun, Debrun and Ostry (2006), IMF Staff Papers 53
  3. Sovereign risk and debt sustainability framework IMF (2022), Staff Guidance Note on the SRDSF

داتا

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.

Sovereign debt paths and the odds of breaching a threshold. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Debt dynamics and the interest-growth differential (Blanchard (2019), Public debt and low interest rates, American Economic Review 109); Stochastic fan charts (Celasun, Debrun and Ostry (2006), IMF Staff Papers 53); Sovereign risk and debt sustainability framework (IMF (2022), Staff Guidance Note on the SRDSF).

لە ناو شەش کۆڵەکەکەدا، بە سەرپەرشتیی Nexus Consortium

06پشتڕاستکردنەوە

How results are checked.

Models are assessed through specification tests, robustness checks and out-of-sample performance. Results are compared across datasets and methods.

بپشکنەHow it is done
RobustnessResults are checked across specifications and samples
EndogeneityMethods to address reverse causality are stated
Out-of-sampleModels are tested on periods not used in estimation

Endogeneity, measurement error and structural change can bias estimates. Relationships may shift under extreme conditions.

07Exposure, loss and capital

What it means for exposure, loss and capital.

The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.

Capital, solvency and fiscal indicators

The indicators a risk officer, supervisor or ministry of finance reads first on econometrics for public finance and sovereign debt.

IndicatorUnitWho reads itFramework
Public debt% of GDPMinistries of finance, IMFDebt Sustainability Framework
Gross financing needs% of GDP per yearMinistries of finance, investorsDebt Sustainability Framework
Contingent liabilities% of GDP, by sourceMinistries of financeFiscal risk statements
Interest to revenue% of revenueMinistries of finance, rating usersPublic finance statistics
Sovereign spreadbasis points over a benchmarkInvestors, debt managersMarket data
CET1 ratio% of risk-weighted assetsBanks, supervisorsBasel framework
Liquidity coverage ratio% of stressed outflowsBanks, central banksBasel framework
Source: the frameworks named in each row. Indicator names, units and frameworks only; values belong to each institution’s own reporting and are never shown here.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
09Stewardship

Who maintains the method.

گەشەپێدانی ئابووری stewards econometrics for the Consortium and reviews how it is applied across platforms.

10پەیوەست

ئەو شوێنانەی ئەم لاپەڕەیە لە ناو کۆنسۆرتیۆم بۆیان دەبات.

11پرسیارەکان

پرسیار و وەڵام.

What are the limits of econometrics in this field?
Endogeneity, measurement error and structural change can bias estimates. Relationships may shift under extreme conditions.
How are results checked?
Models are assessed through specification tests, robustness checks and out-of-sample performance. Results are compared across datasets and methods.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
ئەو canonical method page lists every platform that applies it.
Reportsبەشداربە

لەم لاپەڕەیەوە بەڵگە پێشکەش بکە.

Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

Global Risks Allianceداراییی گشتی و قەرزی سەروەرانەئامادەیی دارایی و بیمەمەترسی و خۆڕاگریی سیستەمی وڵات
داراییی گشتی و قەرزی سەروەرانە

Bring an exposure or capital question to the Alliance.

زانیاری و پرسیار ئەمڕۆ کراوەن. پرسیارێک ژمارەی ئاماژەی پێدەدرێت و هەنگاوی دواتر دیاری دەکات؛ داواکاری، پارەدان، تەرخانکردنی کۆمپیوتەر و تۆمارکردن تەنیا کاتێک دەکرێنەوە کە تیم، مەرج و توانای پێداچوونەوەیان هەبێت.