Asymmetries in economic and financial relationships
Bibliographic record
Abstract
This thesis comprises three essays that show the paramount importance that the presence or absence of asymmetries can play in certain economic and financial relationships.It is shown that whether the distributions underlying the relationships considered are symmetric or not, can be used to explain certain stylized facts and also for inference purposes.The first essay develops simulation-based finite and large sample tests for Markov switching autoregressive models.This class of models has been used successfully in many economic and financial applications to account for regime changes in the mean or variance of a time series.A fundamental question in the application of Markov switching models is whether the data generating process is indeed characterized by regime changes.Under the null hypothesis of a linear model, conventional likelihood-based inference is invalid and can be very misleading.The first essay proposes new tests for Markov switching models based on the technique of Monte Carlo tests.Part of the proposed testing strategy exploits the fact that the marginal distribution of a random variable governed by a Markov switching process is given by a mixture distribution that is characterized by skewness or excess kurtosis relative to a normal distribution.For models without an autoregressive component, the proposed tests are provably exact.For autoregressive Markov switching models, the autoregressive component is first eliminated using consistent estimates of the autoregressive parameters.The proposed testing strategy is then applied to the transformed series and yields an asymptotic test.Simulation results show that the proposed tests reject at their nominal level and have good power.When applied to U.S. GNP growth rates, the tests reject the null hypothesis of a linear model against the Markov switching specification.l n finite-samples under sufficiently general assumptions, that the random walk hypothesis cannot be rejected.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".