The Determinants of Inflation in Botswana and Bank of Botswana’s Medium-Term Objective Range
Bibliographic record
Abstract
This study is motivated by the high and unstable episodes of inflation in Botswana over the last 20 years or so. This is despite the Bank of Botswana’s (BOB) concerted effort to keep inflation at its minimum and stable over time. More specifically, it has been attempting to bring down and keep inflation within the medium-term objective range of 3 to 6percent. The objectives of this study are, therefore, to: (a) examine the determinants of inflation in Botswana by identifying the factors that have influenced its movements over time; and (b) assess the likelihood that the Bank of Botswana’s medium-term objective range of 3 to 6 percent could be achieved in the short to medium-term (one and half to two years). To examine these objectives, the study used an Auto Regressive Distributed Lag (ARDL) estimation technique. A quarterly data ranging from the first quarter of 1990 to the fourth quarter of 2010 is used to estimate the model. And the results show that the identified variables are significant and have the theoretically expected signs. The main conclusions of the study are: (a) price inertia, real GDP, money supply and South African prices play a dominant role in determining inflation in Botswana; and (b) unless international deflationary environment prevails, the probability that the Bank of Botswana will achieve its medium-term objective range of 3 to 6 percent in the medium-term is very low, according to the policy simulation results in this study. Keywords: Inflation, Monetary Policy, forecasting, Simulation and Validation BOJE: Botswana Journal of Economics , 11(15(), 57-74
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".