Bibliographic record
Abstract
We study the characteristics of the real business cycle and the sources of the economic fluctuation in Taiwan over the last forty years, when it experienced both developing and developed stages of the economy, by considering a small open economy real business cycle model with financial friction. In particular, the breaking time point that distinguishes between developing and developed stages of the economy in Taiwan is chosen on the basis of the International Monetary Fund (IMF). We use a Bayesian approach to obtain the posterior densities for the structural parameters of interest. Conditioning on the Bayesian point estimates, the posterior mean in particular, we generate a set of statistical moments and related statistics that characterize the features and sources of the real business cycle. We find that a real business cycle model with financial friction explains the features of real business cycle in a developing stage of Taiwan’s economy well. However, the results it provides are unsatisfactory for matching the characteristics of real business cycle in a developed stage of Taiwan’s economy. In addition, the technology shock explains a large fraction of the economic fluctuation, particularly in real output, consumption and investment. More precisely, permanent technology shock explains a larger fraction of the economic fluctuation than a transitory technology shock.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".