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Record W2167413594

DOMESTIC DEBT, INFLATION AND ECONOMIC CRISES: A PANEL COINTEGRATION APPLICATION TO EMERGING AND DEVELOPED ECONOMIES

2007· article· en· W2167413594 on OpenAlexaboutno aff
Melike Bildirici, Özgür Ömer Ersin

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsInflation (cosmology)DebtCointegrationMonetary economicsDebt-to-GDP ratioEmerging marketsDebt ratioExternal debtInternational economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

The paper aims to investigate the economic relationship between inflation and domestic debt. In countries that experience high inflation, the inflationary process fed on increasing costs of domestic debt. As a result, the increasing debt to GDP ratios led these countries to borrow at higher interest rates and with lower maturity rates. The paper aims to divide countries into three groups. First group consists of Mexico, Turkey and Brazil; countries with high inflation experiences which result in increasing costs of domestic debt. Second group consists of Belgium, Canada and Japan, low inflation rates, low costs of borrowing. Third group consists of Portugal, Greece and Spain, countries with low inflation, high borrowing with low costs of borrowing and fiscal discipline. It is observed that, increasing costs of borrowing is epidemic to those with Non-Ricardian fiscal policies. As a result, it is not the rate of domestic debt/GDP ratio but the cost of borrowing and active fiscal regimes that lessens the immunity of emerging economies to the economic crises. Another important result that cannot be avoided is the fact that, FMOLS and DOLS methods followed in the study resulted in similar estimates for some countries, whereas we also observe very different estimates for others.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.252
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2007
Admission routes1
Has abstractyes

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