Applied Computational Economics and Finance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The book ‘Applied Computational Economics and Finance’ by Mario Miranda and Paul Fackler is a great hands‐on introduction to selected computational methods that are useful in economics and finance. With its emphasis on practical numerical methods, its large number of code fragments, and an accompanying toolbox of MATLAB programs, this book enables economists without much prior knowledge of computational methods to quickly learn about standard numerical techniques and to apply them to their own models. The lucid discussion of many detailed examples illustrates the usefulness and applicability of the covered computational methods and will accelerate the learning process of any interested reader. The book has two quite different parts. The first, which comprises about one‐third of the book, covers basic numerical techniques such as solution methods for linear and nonlinear systems of equations, techniques for finite‐dimensional optimisation problems, and methods for numerical integration and differentiation and the approximation of functions. The second part covers solution methods for both discrete and continuous time dynamic models and illustrates them in a large number of examples. The book is accompanied by ‘CompEcon’, a toolbox of MATLAB programs that is available on the second author's web site.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it