Linear and Nonlinear Relationship Between Real Exchange Rate, Real Interest Rate and Consumer Price Index: An Empirical Application for Countries with Different Levels of Development
Bibliographic record
Abstract
The research population of this study consists of Australia, Azerbaijan, Egypt, Brazil, Chile, Canada, Hungary, Pakistan, India, Ukraine and the United Kingdom. For these countries; T, the relationship between Exchange Rate Index (exc), Real Interest Rate (int) and Consumer Price Index (cpi) variables were examined. Data from 2000Q1 to 2021Q3 were used in the study. The data are taken from the IMF's data bank. Analysis was done in R-Studio. Wo Seasonality Test, Augmented Dickey-Fuller Test, Linear Granger Causality Analysis and Nonlinear Granger Causality Analysis were used to investigate the relationship between variables. The theory claims that there is causality in both directions between exchange rate, interest rate and inflation. In the study, the relationship between these variables was investigated with linear and nonlinear causality tests. It is thought that the empirical results that contradict the theory are caused by the development levels of the countries, their macroeconomic structures, the applied fiscal and monetary policy instruments, the conjuncture and the analysis methods. The study aims to investigate these claims. For this reason, the development levels, sociocultural and socioeconomic structures of the selected countries were requested to be different. In addition, two different test methods, linear and non-linear, were preferred for the causality relationship. It was observed that the selected analysis methods significantly affected the results. Linear causality analysis results are closer to theoretical implications. However, the level of development of the countries does not have a significant effect on the relationship between the variables.
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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 | 0.001 | 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 | 0.000 | 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".