Three Essays on the Study of Persistence via Impulse-Response Confidence Bands with Application to Autoregressive Moving Average (ARMA) Processes
Bibliographic record
Abstract
The two-step simulation-based method introduced in this thesis combines Indirect Inference jointly with Monte Carlo (MC) test methods to deliver identification robust confidence sets for finite samples.In the first chapter, a general framework is introduced, which considers three auxiliary estimating functions: two-sided forward and backward looking regressions with leads and lags, long-autoregressions and empirical autocorrelations.The first simulation stage is incorporated to calibrate parameter estimates.In the second simulation stage, the resulting objective functions are inverted applying the MC test to obtain joint confidence sets.The empirical focus is on Autoregressive Moving Average (ARMA) processes, since identification and boundary issues raise enduring complications for estimation and inference.In particular, simultaneous impulse-responses confidence bands are derived for ARMA processes via confidence set projections.Supporting simulation studies illustrate the accurate size and good power of our method.The following chapters introduce further refinements to our framework with empirical applications.The second chapter investigates the persistence of oil shocks via impulse-response confidence bands for two crude oil benchmarks priced in U.S. dollars per barrel: the West Texas Intermediate (WTI) for high quality light sweet crude oil delivered at Cushing, Oklahoma, and the Western Canadian Select (WCS) for heavy blended high-TAN (acidic) crude, quoted at the Husky terminal in Hardisty, Alberta.Stable distributions are incorporated to be able to capture the heavy tails and asym-I would like to thank my committee members, Professors Marcel Voia from Carleton University and Maral Kichian from University of Ottawa for their helpful comments and support along the way.I greatly benefited from the econometric courses thought by Marcel Voia during my master and doctoral training.I am thankful to Maral Kichian for our conversations on econometrics and her generous advices on career paths.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".