PKR Exchange Rate Forecasting Through Univariate and Multivariate Time Series Techniques
Bibliographic record
Abstract
This study aims to examine and compare the accuracy of time series and econometric forecasting models in the context of the exchange rate as we know that fluctuation in the exchange rate may affect the economic activities at the macro – level. For this purpose, the author has chosen the Pakistani Rupee exchange rate against United States Dollars with the annual data from 1980 to 2018. The results revealed that the exponential model provides the most effective accuracy in forecasting rather than the Naive, ARIMA and ARDL Co-integration model. This paper has also covered the gap of unavailability of literature regarding the application of ARDL and Exponential Smoothing model for the forecasting of the exchange rate in Pakistan. It is also anticipated that historical data do not play a vital role in the forecasting of the future trend of time series i.e. Pakistani Rupees against US Dollars. However, all three-time series anticipated that the recent observations play a significant role in the speculation of the upcoming future trend. Keywords: Forecasting, Exchange Rate, Naïve Model, ARDL Co-Integration model, Econometrics
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".