Demand for Money Function in Case of Philippines: An Empirical Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".