Do exchange rate and inflation matters to Nigerian economy?New evidence from vector autoregressive (VAR) approach
Bibliographic record
Abstract
Currency fluctuations and inflation are the natural norm for most major economies. Numerous factors influenceeconomic growth, including a country’s exchange rate system performance, the outlook for inflation, and interestrate differentials. These are the most significant factors that hinder the economic growth of every nation. As aresult, this analysis investigates the impact of exchange rate and inflation on Nigeria’s growth performance from1986 to 2021. Impulse response and variance decomposition were estimated. The real gross domestic product(RGDP) was used as a proxy for growth performance, while the inflation rate (IFNR), real exchange rate (REXR),and interest rate (INTR) were also used as proxies. The results of impulse response and variance decompositionestimates in the short-run (third quarter) and long-run (tenth quarter) show that real exchange rate D(REXR), INTR,and IFNR all have a positive impact on RGDP variation, with values of 13.38, 31.88, and 22.40%, respectively,in the third quarter. In the long run (the 10th quarter), REXR contributed approximately 28.76% of the variationin RGDP. The interest rate contributed 24.14%, while the IFNR has contributed about 28.27% of the variation inRGDP in the long run. Therefore, summing the contributions of REXR, INTR, and INFR to RGDP, these variablescontributed about 81.17% of the variation in RGDP in the long run. Hence, the research concluded that REXR,INTR, and IFNR have a positive effect on growth performance as proxied by RGDP in Nigeria within the periodof the research. The research recommended that the government should provide a policy that will reduce theexcess growth of aggregate demand (AD) in the economy, which will reduce inflationary pressure, in order toachieve the sustainable development goals (SDGs) of 2030 in Nigeria, which include restoring economic growthand macroeconomic stability through macroeconomic variables such as the exchange rate, inflation, and othersignificant variables.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".