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Record W3013227424 · doi:10.5430/rwe.v11n1p220

Demand for Money Function in Case of Philippines: An Empirical Analysis

2020· article· en· W3013227424 on OpenAlexvenueno aff
Chayanan Kerdpitak

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsGDP deflatorCointegrationBroad moneyGranger causalityMonetary policyReal gross domestic productInterest rateEconometricsMonetary economicsDemand curveOrder (exchange)MacroeconomicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

An effective formulation of monetary policy provides an empirical and coherent model of money related with demand. In order for the monetary authorities to understand the demand for the purpose of money function, the steadiness of money demand is important as it leads towards an application of efficient monetary policy. In order to examine the stability of money demand function of Philippines, following study was conducted with broad money, real asset price index, GDP deflator, real GDP, long-term interest rate and short-term interest rate. For empirical investigation, unit root test, cointegration, and Granger-Causality tests were used. However, the findings of the cointegration suggests that cointegration reveals there is presence of linear combinations, and results shows that there are four cointegrating equations present. Therefore, it is evident that there are at least 4 cointegrating relations between the variables. Hence, some of macroeconomic indicators can be used to predict the broad money due to presence of vector. However, the Granger-Causality shows that no macroeconomic variable granger cause broad money (M1). Therefore, the selected macroeconomic indictors RS, LS, CPI, GDP deflator, RGDP and AP/P cannot be used to predict the variation in the broad money (M1) in case of Philippines. This means the money demand function in Philippines is not stable, and for this purpose further investigation is suggested by increasing sample size and time window in quarterly or semi-annually.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.344
GPT teacher head0.391
Teacher spread0.047 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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