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Record W4205193469 · doi:10.3390/jrfm15010011

Exports and Imports-Led Growth: Evidence from a Small Developing Economy

2022· article· en· W4205193469 on OpenAlexvenueno aff
Humnath Panta, Mitra Lal Devkota, Dhruba Banjade

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationEconomicsInternational economicsError correction modelEmpirical evidenceShort runInternational tradeCausality (physics)Monetary economicsMacroeconomicsEconometrics

Abstract

fetched live from OpenAlex

This paper examines equilibrium relationships and dynamic causality between economic growth, exports, and imports in Nepal using time-series data between 1965 and 2020. This research examines the impact of exports and imports on the economic growth of Nepal and documents empirical evidence in exports-led growth, imports-led growth, growth-led exports, and growth-led imports hypotheses in both the short and long run. The test results show no evidence favoring the exports-led growth and growth-led exports hypotheses in both the short and long run. However, the study finds evidence supporting the imports-led growth hypothesis in the short term and the growth-led imports hypothesis in the long term. Overall, this paper finds no evidence in favor of the notion that foreign trade supports the economic growth of Nepal in the long run. The research findings may have important implications for policymakers in Nepal. The paper contributes to trade and economic growth literature by investigating the relationship between exports, imports, capital, and gross domestic products in a small economy such as Nepal, where exports make a minimal and imports make an extensive contribution to gross domestic products by using cointegration and the vector error correction model.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.697

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 head0.026
GPT teacher head0.201
Teacher spread0.175 · 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 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

Citations30
Published2022
Admission routes1
Has abstractyes

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