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Record W2939796377

Business cycles of India, developed and developing economies: A test of co-movements

2019· article· en· W2939796377 on OpenAlexaboutno aff
Sumanpreet Kaur

Bibliographic record

VenueZENITH International Journal of Multidisciplinary Research · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsIndependence (probability theory)ChinaEconomyDeveloping countryBusiness cycleEconomicsTest (biology)World economyGeographyDevelopment economicsMathematicsEconomic growthPolitical scienceMacroeconomicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

This study aims at casting some light on the co-movements of the Indian economy vis-a-vis some of the developed and developing economies as a contribution to the constant debate on independence of emerging economies business cycles from those of the advanced nations. An attempt has been made to test the proposition of India's independence against the developed group consisting, Australia, Canada, France, Germany, Italy, Japan, U.K. and U.S.A. and the developing group consisting, Argentina, Brazil, China, Hong Kong, Indonesia, Mexico, Malaysia and Qatar. The annual data from 1970 to 2016 has been sourced from World Development Indicators on nominal GDP for calculating output gaps and Euclidean distance for graphical analysis and the econometric analysis for testing the hypothesis. The graphical plots led to the refutation of independence hypothesis for all the economies and the econometric analysis also reveals the presence of business cycle co-movements between India and Australia, Canada, Italy, Great Britain and United States of America from the developed world and for Brazil, China and Qatar from the developing world.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.371
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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