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Record W2166617614 · doi:10.5539/mas.v5n2p192

Oil Price Shocks: A Comparative Study on the Impacts in Purchasing Power in Pakistan

2011· article· en· W2166617614 on OpenAlexvenueno aff
Muhammad Bachal Jamali, Asif Ali Shah, Hassan Jawad Soomro, Kamran Shafiq, Faiz Muhammad Shaikh

Bibliographic record

VenueModern Applied Science · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsExchange rateVariance decomposition of forecast errorsInterest rateVector autoregressionOil pricePrice levelMonetary economicsMoney supplyShock (circulatory)MacroeconomicsEconometrics

Abstract

fetched live from OpenAlex

The current research investigates the relationship between changes in crude oil prices and Pakistan and the macro-economy. A multivariate VAR analysis is carried out among five key macroeconomic variables: real gross domestic product, short term interest rate, real effective exchange rates, long term interest rate and money supply. From the VAR model, the impulse response functions reveal that oil price movements cause significant reduction in aggregate output and increase real exchange rate. The variance decomposition shows that crude oil prices significantly contribute to the variability of real exchange rate long term interest rate in the Pakistan economy while oil price shocks are found to have significant effects on money supply and short term interest rate in the economy. Despite these macro econometric results, caution must be exercised in formulating energy policies since future effects of upcomming oil shocks will not be the same as what happened in the past. Explorations and development of practicable alternatives to imported fuel energy will cushion the economy from the repercussions of oil shocks. Oil price shock has negative impact on the GDP and as well as economy of Pakistan.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.076
GPT teacher head0.290
Teacher spread0.213 · 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

Citations11
Published2011
Admission routes1
Has abstractyes

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