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Record W4385666532 · doi:10.21833/ijaas.2023.08.002

Macroeconomic determinants of the real exchange rate in Pakistan

2023· article· en· W4385666532 on OpenAlexaboutno aff
Kashif Munir, Mehwish Iftikhar

Bibliographic record

VenueInternational Journal of ADVANCED AND APPLIED SCIENCES · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateOpenness to experienceEconomicsDistributed lagMonetary economicsCurrencyProductivityQuarter (Canadian coin)Effective exchange rateContext (archaeology)Money supplyMacroeconomicsInterest rateEconometrics

Abstract

fetched live from OpenAlex

This research endeavors to comprehensively examine the macroeconomic determinants that influence the real exchange rate in Pakistan over an extended temporal horizon. By employing quarterly data spanning from the first quarter of 1980 to the fourth quarter of 2020, this study employs the autoregressive distributed lag (ARDL) methodology to discern both immediate and enduring determinants of the real exchange rate. The findings distinctly reveal that variables such as money supply, trade openness, workers' remittances, and productivity collectively constitute the long-term determinants of the real exchange rate in the context of Pakistan. Specifically, an augmentation in money supply and an escalated level of economic openness manifestly contribute to the reduction of the real exchange rate, whereas an inflow of remittances and heightened productivity exhibit the propensity to elevate the real exchange rate over an extended duration. This exploration underscores the imperative for proactive engagement by the nation's monetary authorities within the foreign exchange market, with the overarching objective of preserving the currency's value at a state of equilibrium and, thereby, ensuring the holistic integrity of the economy.

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 score0.508
Threshold uncertainty score0.168

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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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