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Releases: bluearf/openeconometrics
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Python descriptions: SDK 0.3.20a2 and charts 0.3.2a2
This alpha refresh corrects the PyPI descriptions and installation commands for SDK 0.3.20a2 and openecon-charts 0.3.2a2.
The scientific, chart and frontend source is identical to the published 0.3.20a1 / 0.3.2a1 pair apart from package version literals. The frozen archives are tested as a pair in clean Python 3.11 and 3.13 environments, with their original CI execution receipts included below. The prior full scientific acceptance applies to the unchanged runtime source; this refresh does not claim a new full numerical run.
Install the SDK from PyPI after publication:
python -m pip install --pre "openecon==0.3.20a2"Or install the standalone chart library:
python -m pip install --pre "openecon-charts==0.3.2a2"Accepted package execution: https://github.com/bluearf/openeconometrics/actions/runs/39517821987 (Python 3.11 and 3.13; same four archives, 324 SDK regression cases plus installed metadata and sandboxed chart export checks per minor).
Teaching labs 0.2.0 · Rewritten Statistics, Microeconomics and Advanced courses
The 80 English teaching labs span four courses: Econometrics, Statistics, Microeconomics and Advanced Econometrics. Each course has separate student and instructor packages.
What changed
This release rewrites the Statistics, Microeconomics and Advanced Econometrics courses, 60 labs in all.
- Handouts: every handout and instructor companion is written for its own lab, with no shared template text.
- Each handout names its unit and sample, develops the method with the lab's own numbers, reads the output and the figure, and states what the evidence supports and what it does not.
- Each ends with new exercises. Their answers are only in the instructor edition.
- Journal cases and references: each handout closes with a case from a published study and a list of journal articles. No textbook material is used.
- Every DOI was checked against Crossref.
- Every finding the text states appears in the article's abstract.
- Charts: figures are drawn with OpenEconometrics Charts from each lab's saved results, and they match the charts the application shows.
- Several charts now show what their titles name:
- labor demand against the wage (Microeconomics 09);
- the marginal cost line through minimum average cost (Microeconomics 10);
- vertical summation with marginal cost (Microeconomics 18);
- Bonferroni pairwise intervals (Statistics 17);
- ratings split by service (Statistics 18).
- Each Advanced Econometrics lab charts its own chapter's concept.
- Several charts now show what their titles name:
- Dictionary fixes: the variable dictionaries of Advanced Labs 12 and 20 no longer call raw columns centered. Their data are unchanged.
What did not change
- Numbers, tables and checks are unchanged from the verified references.
- All 60 rewritten labs pass the course verifier, including every Python block in the handouts.
- The Econometrics course is rebuilt from current source without content changes.
Packages
- Each student ZIP contains:
- a navigable offline course and a combined reading PDF;
- 20 workbooks, runnable scripts, figures and LaTeX table fragments.
- Each instructor ZIP adds worked answers, full numerical references and the original data generators.
- Student archives exclude instructor material.
delivery-manifest.jsonbinds the eight archives to their private and public source commits.SHA256SUMSlists every file's checksum.
Lesson slides · Econometrics
Slides for the video lessons of the Econometrics course: one page per lesson scene. The text in the PDFs can be selected and searched.
| File | Lab | Pages |
|---|---|---|
openecon-lab01-slides.pdf |
Lab 01 · The Economic Question Comes First | 16 slides |
openecon-lab02-slides.pdf |
Lab 02 · A First Look at Wages | 17 slides |
openecon-lab03-slides.pdf |
Lab 03 · What Changes Across Random Samples? | 15 slides |
openecon-lab04-slides.pdf |
Lab 04 · Does Class Size Predict Test Scores? | 15 slides |
openecon-lab05-slides.pdf |
Lab 05 · How Precise Is Our Estimate? | 14 slides |
openecon-lab06-slides.pdf |
Lab 06 · What Changes When We Add Controls? | 14 slides |
openecon-lab07-slides.pdf |
Lab 07 · Do Groups Have Different Wage Profiles? | 14 slides |
openecon-lab08-slides.pdf |
Lab 08 · Modeling Percentage Changes and Curvature | 14 slides |
openecon-lab09-slides.pdf |
Lab 09 · Same Coefficient, Different Standard Error? | 14 slides |
openecon-lab10-slides.pdf |
Lab 10 · Testing Several Claims Together | 13 slides |
openecon-lab11-slides.pdf |
Lab 11 · Comparing Firms to Themselves | 14 slides |
openecon-lab12-slides.pdf |
Lab 12 · Explaining Labor-Force Participation | 15 slides |
openecon-lab13-slides.pdf |
Lab 13 · Estimating Effects with an Instrument | 14 slides |
openecon-lab14-slides.pdf |
Lab 14 · Evaluating a Randomized Training Program | 14 slides |
openecon-lab15-slides.pdf |
Lab 15 · Before and After a Policy Reform | 14 slides |
openecon-lab16-slides.pdf |
Lab 16 · What Happens Around an Eligibility Cutoff? | 13 slides |
openecon-lab17-slides.pdf |
Lab 17 · The Trap of Trending Series | 13 slides |
openecon-lab18-slides.pdf |
Lab 18 · Accounting for Persistent Shocks | 13 slides |
openecon-lab19-slides.pdf |
Lab 19 · Forecasting Inflation Without Looking Ahead | 14 slides |
openecon-lab20-slides.pdf |
Lab 20 · From Research Question to Finished Report | 13 slides |
The numbers on the slides come from running each lab's lab.py and match its reference.json. All data are synthetic and generated for teaching. The lesson narration was generated with ElevenLabs. The handouts, Python files and workbooks are in teaching/labs/, and the course packages are in teaching-v0.1.1.
Released under Apache 2.0, like the labs. Later lessons will be added to this release series.
Lesson slides · Statistics
Slides for the video lessons of the Statistics course: one page per lesson scene. The text in the PDFs can be selected and searched.
| File | Lab | Pages |
|---|---|---|
openecon-stat01-slides.pdf |
Lab 01 · What Does One Row Represent? | 12 slides |
openecon-stat02-slides.pdf |
Lab 02 · Which Average Answers the Question? | 10 slides |
openecon-stat03-slides.pdf |
Lab 03 · How Variable Are Delivery Times? | 11 slides |
openecon-stat04-slides.pdf |
Lab 04 · Which Denominator Belongs in a Probability? | 11 slides |
openecon-stat05-slides.pdf |
Lab 05 · What Does a Quality Alert Tell Us? | 12 slides |
openecon-stat06-slides.pdf |
Lab 06 · How Many Orders Arrive in Ten Offers? | 11 slides |
openecon-stat07-slides.pdf |
Lab 07 · From Parcel Weights to Standard Scores | 11 slides |
openecon-stat08-slides.pdf |
Lab 08 · Why Do Averages Vary Less Than Observations? | 11 slides |
openecon-stat09-slides.pdf |
Lab 09 · What Does a Confidence Interval Cover? | 11 slides |
openecon-stat10-slides.pdf |
Lab 10 · Is the Filling Process Centered on Its Target? | 12 slides |
openecon-stat11-slides.pdf |
Lab 11 · How Uncertain Is an Opt-In Rate? | 10 slides |
openecon-stat12-slides.pdf |
Lab 12 · Comparing Two Delivery Methods | 11 slides |
openecon-stat13-slides.pdf |
Lab 13 · The Same Workers Before and After | 11 slides |
openecon-stat14-slides.pdf |
Lab 14 · Did Two Messages Produce Different Response Rates? | 11 slides |
openecon-stat15-slides.pdf |
Lab 15 · Do Study Hours and Scores Move Together? | 12 slides |
openecon-stat16-slides.pdf |
Lab 16 · Are Shopping Channels and Membership Independent? | 11 slides |
openecon-stat17-slides.pdf |
Lab 17 · Comparing Three Training Formats | 11 slides |
openecon-stat18-slides.pdf |
Lab 18 · Comparing Ordinal Customer Ratings | 10 slides |
openecon-stat19-slides.pdf |
Lab 19 · Resampling the Observed Baskets | 11 slides |
openecon-stat20-slides.pdf |
Lab 20 · From a Question to a Statistical Report | 13 slides |
The numbers on the slides come from running each lab's lab.py and match its reference.json. All data are synthetic and generated for teaching. The lesson narration was generated with ElevenLabs. The handouts, Python files and workbooks are in teaching/statistics/labs/, and the course packages are in teaching-v0.1.1.
Released under Apache 2.0, like the labs. Later lessons will be added to this release series.
Lesson slides · Microeconomics
Slides for the video lessons of the Microeconomics course: one page per lesson scene. The text in the PDFs can be selected and searched.
| File | Lab | Pages |
|---|---|---|
openecon-micro01-slides.pdf |
Lab 01 · Scarcity and Increasing Opportunity Cost | 11 slides |
openecon-micro02-slides.pdf |
Lab 02 · Comparative Advantage and Trading Prices | 11 slides |
openecon-micro03-slides.pdf |
Lab 03 · Market Equilibrium and a Demand Shift | 11 slides |
openecon-micro04-slides.pdf |
Lab 04 · Point Elasticity and Total Revenue | 11 slides |
The numbers on the slides come from running each lab's lab.py and match its reference.json. All data are synthetic and generated for teaching. The lesson narration was generated with ElevenLabs. The handouts, Python files and workbooks are in teaching/microeconomics/labs/, and the course packages are in teaching-v0.1.1.
Released under Apache 2.0, like the labs. Later lessons will be added to this release series.
Lesson slides · Advanced Econometrics
Slides for the video lessons of the Advanced Econometrics course: one page per lesson scene. The text in the PDFs can be selected and searched.
| File | Lab | Pages |
|---|---|---|
| – | Slides are added here as each lesson is published | – |
The numbers on the slides come from running each lab's lab.py and match its reference.json. All data are synthetic and generated for teaching. The lesson narration was generated with ElevenLabs. The handouts, Python files and workbooks are in teaching/advanced-econometrics/labs/, and the course packages are in teaching-v0.1.1.
Released under Apache 2.0, like the labs. Later lessons will be added to this release series.
Python SDK 0.3.20a1 and Charts 0.3.2a1
OpenEconometrics SDK 0.3.20a1 and openecon-charts 0.3.2a1, built once from source a805870d971c0d571f6d5dec79e914b91d6479aa.
The attached four distributions are the exact bytes installed and tested on Linux Python 3.11/3.13 and macOS. Acceptance includes isolated wheel/source installs, package examples, complete HC3 covariance and saved-result replay, and 88 sandboxed chart export checks for each charts distribution on each tested platform.
The exact packaged source passed the registered 171-selector scientific gate on both Python minors: 14,667 cases per minor, no failures, errors or skips, and the unchanged 900-second cohort limit. Source, package provenance and bounded acceptance receipts are attached.
This alpha package release does not establish vendor parity, CUDA acceptance, native installer acceptance or cloud-team acceptance. Desktop release evidence is maintained separately. Source and dependencies retain their licenses and notices. Teaching labs remain available in the public repository's teaching directory.
Teaching labs 0.1.1 · 80 labs and IV reference precision
80 English teaching labs across Econometrics, Statistics, Microeconomics and Advanced Econometrics, with named prepared workbooks and separate student/instructor packages.
This patch improves the independent IV reference in Econometrics Lab 13. Direct exactly identified moment equations avoid amplifying rounding error in the deliberately weak-instrument example. Workbook values, model results, student conclusions and numerical tolerances are unchanged. The revised instructor guide includes the derivation; its rendered PDF was checked.
The four series retain 20 labs each and 79 prepared workbooks in total; data generation remains part of Econometrics Lab 03. The manifest binds the delivery to integrated source and all ZIP checksums. Student archives exclude worked answers and instructor verification material.
80 English teaching labs — four courses
Four English courses with 20 chapters each: Econometrics, Statistics, Microeconomics and Advanced Econometrics. Read the course catalogue.
Each student ZIP contains an offline HTML course, a combined PDF, runnable Python, computed results, equations, figures, LaTeX tables and exercises. Instructor ZIPs separately add worked answers, original synthetic data mechanisms and verification references. There are 79 named Excel workbooks; Econometrics Lab 03 generates repeated samples because simulation is its topic. All examples and data are original synthetic teaching material under Apache-2.0.
Download the student edition for classroom distribution. The instructor editions contain answers. Extract a ZIP and open index.html, or read the combined PDF. Import the chapter workbook into OpenEconometrics without renaming it and run the complete lab.py in a new Python document. The exact reviewed source revision is recorded in delivery-manifest.json; separately installed older SDKs can have different method contracts.
Fresh/native execution, independent numerical references, covariance/inference/sample/saved-state checks, workbook identity, student snippets, HTML/PDF rendering and separate archive checks passed. SHA256SUMS records the eight archive hashes. This teaching release does not upgrade the desktop application or the fixed previously published Python package pair.
OpenEconometrics Codex plugin 0.1.3
Install the local OpenEconometrics plugin in Codex to inspect imported datasets, fit supported models, manage background analyses and read saved results. Supported runtimes return publication LaTeX, inference/sample notes and package requirements without refitting.
Extract openeconometrics-codex-0.1.3.zip and follow INSTALL.md. The archive includes a complete local marketplace and ten source-identical plugin files. Configure your own existing local runtime/workspace; no research data, credentials or runtime are included. Restart the Codex connection after installation.
Version 0.1.3 points to the public repository and preserves plugin identity. 154 focused cases, manifest checks, ZIP readback and two fresh Codex connections passed. Saved synthetic OLS readback is identical across restart and keeps the existing workspace unchanged. Public provenance records the exact package hash.
The selected runtime determines methods and optional LaTeX support. Older separately installed runtimes may provide structured output until upgraded. This plugin archive does not upgrade the desktop app, establish Windows execution or provide an account marketplace listing.