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Record W3007759307 · doi:10.1080/15140326.2020.1722384

Merchandise exports and economic growth: multivariate time series analysis for the United Arab Emirates

2020· article· en· W3007759307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCointegrationEconomicsOrdinary least squaresGranger causalityJohansen testCausality (physics)EconometricsAutoregressive modelWald testShort runMultivariate statisticsTime seriesVector autoregressionError correction modelMacroeconomicsMathematicsStatistical hypothesis testingStatistics

Abstract

fetched live from OpenAlex

This paper examines the validity of the export-led growth (ELG) hypothesis in the United Arab Emirates (UAE) over the period 1975-2012, using a neoclassical production function augmented with merchandise exports and imports of goods and services. The study applies the Johansen cointegration technique and dynamic ordinary least squares (DOLS) regression to confirm the existence of a long-run relationship between exports and economic growth, while the multivariate Granger causality test is applied to examine the direction of the short-run causality. In addition, the existence of long-run causality is investigated by applying a modified version of the Wald test in an augmented vector autoregressive model. The Johansen test and DOLS results confirm the existence of a long-run relationship between exports and economic growth. In addition, the study provides evidence to support the validity of the ELG hypothesis in the short-run, while no long-run causality is found to exist.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.208
Teacher spread0.159 · 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