Oil Prices, the Macroeconomy and Financial Markets
Bibliographic record
Abstract
A large body of research suggests that oil price shocks have significant impacts on economies, with diverse consequences depending on the relative production and use and consequently exports and imports. In the main, the expectation is that an oil price increase is positive for oil-exporter countries but negative for oil-importer countries. In fact, some early studies revealed that oil price increases preceded all recessions other than in the 1960s. However, by the mid-1980s, possibly because of the diminishing role of oil in real economic activity, these models began to lose their explanatory power, such that there has been considerable less effort directed at alternative modelling approaches to this fundamental relationship, not least outside the US. The main question posed in this thesis concerns the nature of the causal relationships between oil prices, the macroeconomy and financial markets and whether they vary between oil importers or exporters. To respond, we first identified a representative sample of large net oil-producer and oil-consumer economies, the former comprising Canada, Norway and Mexico and the later including Brazil, Denmark, Germany, Italy, the Netherlands, Sweden, and the US. We then specified a set of key macroeconomic and financial market variables including the consumer price index, the real exchange rate, monetary aggregates, industrial production, short-term real interest rates and share prices. Our monthly data covers the period 1986M5–2013M1. Finally, to consider the linear and non-linear macroeconomic and financial market responses to oil price movements, we employed a variety of time-series and panel data models including unit root tests, bias-corrected least squares dummy variables model, Westerlund panel co- integration test, linear causality tests, and parametric and non-parametric nonlinear tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".