Macro Simulations for PCs in the Classroom
Bibliographic record
Abstract
There has always been a large difference between macroeconomics in the classroom and macroeconomics as it is used in practice. Macroeconomics of the classroom is basi-cally theoretical, and it is taught almost exclusively in a simple comparative stat-ics framework. Practicing macroeconomists, on the other hand, work with time-series data, sophisticated statistical techniques, and large-scale macroeconometric models. The PC revolution has now made it possible to bring macroeconometric models into the classroom. The model in Fair (1984), which consists of 128 equations, has been programmed to run on a PC; this paper will discuss its potential for use as a teaching tool at the elementary and advanced levels. The experi-ence so far has been quite encouraging. I. Hardware Requirements The software was written with the aim of minimizing the amount of memory needed to run the program. This amount turned out to be 128K, which includes the memory needed for the operating system of the computer. Although the model is fairly large (238 en-dogenous and exogenous variables), only five quarters ’ worth of data are needed in core memory at any one time. Data for five quarters are needed because the model has lagged values of up to four quarters. After the model is solved for a given quarter (the fifth quarter in memory), the results for that quarter are written to the disk, a new quarter’s worth of data are read (with the old first-quarter’s data dropped from memory),
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.021 |
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".