An Investigation into the Sources of Depreciations in Mongolian Tugrik Exchange Rate: A Structural VAR Approach
Bibliographic record
Abstract
This paper empirically investigates the sources of fluctuations in real and nominal Mongolian Tugrik (MNT) exchange rates by estimating the structural vector autoregressive (SVAR) model over the period January 1994–May 2021 and decomposing the exchange rate series into stochastic components induced by real and nominal shocks under the assumption of the long-run neutrality of nominal shocks on the real exchange rate level. The empirical results show that the real MNT exchange rate movements are primarily due to the real shocks, while the nominal shocks have a major role in explaining nominal exchange rate movements in the short and long run. The nominal exchange rate shows a delayed over-shooting occurring between one and three years after a nominal shock hits the economy. The long-run effect of a monthly one standard deviation nominal shock on nominal MNT exchange rate is 2.5%, which results in a permanent divergence between real and nominal MNT exchange rate and causes non-cointegrated relation between real and nominal MNT exchange rates. The historical decomposition of forecast error indicates that the nominal shock plays a significant role in explaining the depreciation in nominal MNT exchange rate over the last three decades. Our recommendation is to stop “cash handling” policy, minimize monetary shock, and coordinate fiscal and monetary policies to avoid large nominal depreciation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".