Monetary Conditions Index: A Composite Measure of Monetary Policy in Pakistan
Bibliographic record
Abstract
Accurate measures of the size and direction of changes in monetary policy are very important. A number of variables/indicators have been used as a measure of the stance of monetary policy the world over. These include growth rates of monetary aggregates and credit aggregates, short-term interest rate as used by Sims (1992), index of minutes of Federal Open Market Committee (FOMC), as suggested by Friedman and Schwartz (1963) and reintroduced by Romer and Romer (1989), monetary policy index constructed by employing Vector Autoregression (VAR) estimation technique with prior information from Central Bank such as Bernanke and Blinder (1992) and Bernanke and Mihov (1998), and Monetary Conditions Index (MCI)—which is the focus of this paper—constructed by and used by Bank of Canada [Freedman (1995)], taking into consideration the interest rate and exchange rate channel of monetary policy transmission mechanism in a small open economy. In case of open economy it is assumed that the monetary policy affects the economy and the prime objective of monetary policy, rate of inflation, through two important transmission mechanisms. These transmission channels are; interest rate channel and exchange rate channel. The working of the first channel is that the interest rate influences the level of expenditures, investment and subsequently domestic demand. The change in official interest rate effects the market rates of interest both short term as well as long term interest rates. This change in market rates of interest is transmitted to the bank lending rates and saving rates. The change in saving rate effects the spending behaviour of individuals (consumption) whereas the change in bank lending rate effects the investment behaviour of firms (investment). The change in aggregate consumption and investment has direct link to the gross domestic product (GDP).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".