Bibliographic record
Abstract
The revised edition of this book captures new developments in economics and finance. Turning its focus towards the application of Engle's (1982) autoregressive conditional heteroscedasticity (ARCH) in cutting-edge research and a discussion of whether energy prices reflect long memory, this book will keep readers up-to-date with current developments in the literature. It presents twenty-one empirical studies of econometric time series analysis of crude oil, natural gas and electricity markets in face of the rapidly changing dynamics of the energy markets. Amongst them, several studies employ nonlinear time series methods, unlike the standard linear approach commonly used, to reflect the nonlinear nature of the economic system. Two new chapters are included, extending beyond the leading-edge research and innovative energy markets econometrics detailed in the first edition: Chapter 17 examines the effects of oil price changes and speculations on economic activity and Chapter 20 re-evaluates empirical evidence for random walk type behavior in energy futures prices using a statistical physics approach. Contents: Crude Oil Markets: Unit Root Behaviour in Energy Futures Prices Rational Expectations, Risk and Efficiency in Energy Futures Markets Maturity Effects in Energy Futures Business Cycles and the Behavior of Energy Prices A Cointegration Analysis of Petroleum Futures Prices Natural Gas Markets: Is There an East¨CWest Split in North American Natural Gas Markets? Business Cycles and Natural Gas Prices Futures Trading and the Storage of North American Natural Gas Electricity Markets: Power Trade on the Alberta-BC Interconnection Imports, Exports, and Prices in Alberta's Deregulated Power Market Cointegration Analysis of Power Prices in the Western North American Markets Crude Oil, Natural Gas, and Electricity Markets: The Cyclical Behavior of Monthly NYMEX Energy Prices The Message in North American Energy Prices Testing for Common Features in North American Energy Markets Volatility Modelling in Energy Markets: Returns and Volatility in the NYMEX Henry Hub Natural Gas Futures Market Measuring and Testing Natural Gas and Electricity Markets Volatility: Evidence from Alberta's Deregulated Markets Volatility in Oil Prices and Manufacturing Activity: An Investigation of Real Options Chaos, Fractals, and Random Modulations in Energy Markets: The North American Natural Gas Liquids Markets are Chaotic Random Fractal Structures in North American Energy Markets The Hurst Exponent in Energy Futures Prices Randomly Modulated Periodic Signals in Alberta's Electricity Market Readership: Upper-level undergraduates, graduates, academics and practitioners in energy economics and applied econometrics. Key Features: A current and invaluable guide to state-of-the-art modeling techniques in energy economics Two new chapters included, one examines how oil price changes and speculations influence the economy, the other re-evaluates if energy futures prices show random walk behavior via a statistical physics approach
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".