MétaCan
Menu
Back to cohort
Record W2157958362 · doi:10.30541/v42i4iipp.987-1014

Money-income Link in Developing Countries: a Heterogeneous Dynamic Panel Data Approach

2003· article· en· W2157958362 on OpenAlexaboutno aff
Azhar Iqbal, Muhammad Sabihuddin Butt

Bibliographic record

VenueThe Pakistan Development Review · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCausality (physics)Granger causalityEconometricsPanel dataStock (firearms)BengaliMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

The question whether real money causes real output appears to be important for many economists working in the area of macroeconomics and, has been subjected to a variety of modern econometric techniques, producing conflicting results. One often applied method to investigate the empirical relationship between money and real activity is Granger causality analysis [Granger (1969)]. Using this approach, the causality question can be sharply posed as whether past values of money help to predict current values of output. This concept, however, should be clearly distinguished from any richer philosophical notion of causality [cf. Holland (1986)]. Present paper examines the relationship between money (both M1 and M2) and income (Real GDP) for 15 developing countries using a newly developed heterogeneous dynamic panel data approach.1 Sims (1972) postulated “the hypothesis that causality is unidirectional from money to income agrees with the post war U.S. data, whereas the hypothesis that causality is unidirectional from income to money is rejected”. Since then a voluminous literature has emerged testing the direction of causality.2 Some studies have tested the relationship between these variables and the direction of causality for a particular country using time series techniques [e.g., Hsiao (1979) for Canada, Stock and Watson (1989) for U.S. data, Friedman and Kuttner (1992, 1993) for U.S. data, Thoma (1994) for U.S. data, Christiana and Ljungquist (1988) for U.S. data, Davis and Tanner (1997) for U.S. data, Jusoh (1986) for Malaysia, Zubaidi, et al. (1996) for Malaysia, Biswas and Saunders (1998) for India, and Bengali, et al. (1999) for Pakistan]. Other studies have tested the above on a number of countries, for example Krol and Ohanian (1990) used the data for Canada, Germany, Japan and the U.K. Hayo (1999) using data from 14 European Union (EU) countries plus Canada, Japan, and the United States. More recently Hafer and Kutan (2002) used a sample of 20 industrialised and developing countries. This paper contributes to this later strand of the literature, which it extends in three directions. First, it employed a newly developed panel cointegration technique [Larsson, et al. (2001)], to examine the long-run relationship between money and income. Second, the study performs panel causality test, recently developed by Hurlin and Venet (2001), to explore the direction of causality between the said variables. Third, the important contribution of the present study is to test whether relationship between money and income is homogeneous or heterogeneous across countries.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.293
Teacher spread0.157 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Pakistan Development ReviewSame topicMonetary Policy and Economic ImpactFrench-language works237,207