On the Liquidity Effect of Monetary Policy in the CEMAC Countries: An Empirical Investigation
Bibliographic record
Abstract
The aim of this paper is to investigate on the effectiveness of the liquidity effect of monetary policy actions in the CEMAC region. As the conventional wisdom states, a cornerstone for the central bank to stimulate the economy is to lower interest rates by increasing the supply of narrow money. However, a number of empirical studies argue that monetary shocks rather raise than lower short term interest rates. Among problems are the choice of the appropriate measure of money and the set of identifying assumptions about policy shocks. In this study we take an account of these difficulties and adopt a methodology advocated by Christiano and Eichenbaum(1991) which seems appropriate in the special case of the CEMAC countries. Our results show that apart from Cameroon, the leading economy of the region, the liquidity effect is not effective in the region. Regardless of the set of identification scheme used, there is some evidence of liquidity effects in Cameroon. In the other countries, either this effect is absent (Chad) or the effect is preceded by a liquidity puzzle, admits a price puzzle and some counterfactual effects on output (Central African Republic, Congo, Equatorial Guinea and Gabon). A related question on the liquidity effect literature is the loanable funds effect, as put forwards by Friedman and Schwartz (1982). We argue that as the liquidity effect, there is no real support of a loanable effect in the CEMAC area.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".