The Fisher Effect in an Emerging Economy: The Case of India
Bibliographic record
Abstract
The objective of this study is to test the relationship between short-term nominal interest rate and inflation in the context of the Indian financial market. To achieve this objective we perform Augmented Dickey-Fuller unit root test to check for stationarity and thereafter we test for co-integration using the Engle-Granger method and further corroborate the findings of this test with the Johansen-Juselius method. Lastly, we perform the Granger causality test. Monthly data of inflation and nominal short term interest rates for the period from April 1996 to August 2004 were used. We find that expected inflation and nominal short-term interest rates are co-integrated in the Indian context. Thus, the present study doesn’t reject the Fisher effect in the Indian financial market. This test shows that expected inflation is Granger caused by nominal short term interest rates. These findings are important in the context of financial market policies in emerging economies like India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".