{"id":27248661,"url":"https://github.com/v-mayya/programming-statistics-and-econometrics-resources","last_synced_at":"2026-04-29T09:33:56.785Z","repository":{"id":210560548,"uuid":"726869669","full_name":"V-Mayya/Programming-Statistics-and-Econometrics-Resources","owner":"V-Mayya","description":"Programming, statistics and econometrics resources ","archived":false,"fork":false,"pushed_at":"2023-12-05T02:31:31.000Z","size":1153,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-12-05T17:30:28.672Z","etag":null,"topics":["econometrics","programming","python","r","statistics"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/V-Mayya.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-12-03T16:36:40.000Z","updated_at":"2023-12-05T17:30:28.673Z","dependencies_parsed_at":"2023-12-03T17:38:47.503Z","dependency_job_id":null,"html_url":"https://github.com/V-Mayya/Programming-Statistics-and-Econometrics-Resources","commit_stats":null,"previous_names":["v-mayya/programming-statistics-and-econometrics-resources"],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/V-Mayya%2FProgramming-Statistics-and-Econometrics-Resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/V-Mayya%2FProgramming-Statistics-and-Econometrics-Resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/V-Mayya%2FProgramming-Statistics-and-Econometrics-Resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/V-Mayya%2FProgramming-Statistics-and-Econometrics-Resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/V-Mayya","download_url":"https://codeload.github.com/V-Mayya/Programming-Statistics-and-Econometrics-Resources/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248317732,"owners_count":21083525,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["econometrics","programming","python","r","statistics"],"created_at":"2025-04-10T23:40:58.419Z","updated_at":"2026-04-29T09:33:51.735Z","avatar_url":"https://github.com/V-Mayya.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Programming, Statistics, and Econometrics Resources\n\n--- \n\n```python\n# 📌 Find\nresources = [\"Books\", \"Websites\", \"Other Repos\", \"Academic Papers\"]\n\n# 📌 on these \ntopics = {\"Programming\": [\"Python\", \"R\"],\n\n          \"Statistics\": [\"Coming Soon!\"],\n\n          \"Econometrics\": [\"OLS Regression Analysis \u0026 Time Series\",\n                           \"Microeconometrics \u0026 Statistical Models\",\n                           \"Applied Econometrics \u0026 Causal Inference\",\n                           \"Computational Methods in Econometrics\"],}  \n```\n\nSelect the titles to go to the relevant sections.\n\n### Resources \n\u003cdetails close\u003e\n\u003csummary\u003e \u003cb\u003e Programming \u003c/b\u003e 🧑‍💻 \u003c/summary\u003e\n\u003cbr\u003e \n\n\u003ca name=\"contents_prog\"\u003e\u003c/a\u003e \n## Contents\n\n📌 **Python**\n- [Fundamentals](#fundamentals_py)\n- [Data analytics \u0026 Data Science](#data_py)\n- [Machine Learning](#machine_py)\n- [Algorithms \u0026 Data Structures](#algo_py)\n- [Apps + Others](#apps_py)\n \n📌 **R**\n- [Fundamentals](#fundamentals_r)\n- [Data analytics \u0026 Data Science](#data_r)\n- [Machine Learning](#machine_r)\n- [Algorithms \u0026 Data Structures](#algo_r)\n- [Apps + Others](#apps_r)\n\n(currently Python and R primarily)\n\n\u003ca name=\"fundamentals_py\"\u003e\u003c/a\u003e  \n## 🎯 Fundamentals  \n\u003e Python fundamentals: books, websites and other github repos\n\n‣ Books 📚 \n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n|  | |  | | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Python Cheatsheet | For quick reference, covering various topics (loops, functions, OOP and more). Based on 'Automate the Boring Stuff with Python' book and other sources. | [Link](https://www.pythoncheatsheet.org/) | Learn |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | ---- | --- |\n| Full Speed Python | For self-learners with topics and exercises | [Link](https://github.com/joaoventura/full-speed-python) | Learn + Practice (exercises from the Superior School of Technology of Setúbal) |  \n| Paragraph | Text | Link | Practice |\n\n[Back to contents](#contents_prog)\n\n\u003ca name=\"data_py\"\u003e\u003c/a\u003e  \n## 🎯 Data analytics \u0026 Data Science \n\u003e Python data analytics and data science resources: books, websites and other github repos\n\n‣ Books 📚 \n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| PandasAI | Combining data analysis with AI and making the process conversational! | [Link](https://docs.pandas-ai.com/en/latest/) | Practice |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Awesome Public Datasets | A list of public datasets in various domains (ranging from climate to cancer)| [Link](https://github.com/awesomedata/awesome-public-datasets#economics) | Practice | \n\n[Back to contents](#contents_prog)\n\n\u003ca name=\"machine_py\"\u003e\u003c/a\u003e  \n## 🎯 Machine Learning \n\u003e Machine Learning in Python: books, websites and other github repos\n\n‣ Books 📚 \n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n|  | |  | | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | ---- | --- |\n| Paragraph | Text | Link | Practice |\n\n[Back to contents](#contents_prog) \n\n\u003ca name=\"algo_py\"\u003e\u003c/a\u003e \n## 🎯 Algorithms \u0026 Data Structures \n\u003e Python algorithms and data structures: books, websites and other github repos\n\n‣ Books 📚\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| The Algorithms | Search up any algorithm to find out more | [Link](https://github.com/TheAlgorithms/Python) | Learn |\n| Advanced Data Structures with Python | Algorithms and data structures uses and examples, especially useful for competitive programming | [Link](https://github.com/bhavinjawade/Advanced-Data-Structures-with-Python) | Learn |\n\n[Back to contents](#contents_prog)\n\n\u003ca name=\"apps_py\"\u003e\u003c/a\u003e \n## 🎯 Apps + Others\n\u003e Python apps + other areas: books, websites and other github repos\n\n‣ Books 📚\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| ReactPy | Text | [Link](https://reactpy.dev/docs/index.html) | Practice |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn |\n| PyWebIO | Building web applications without the need for HTML and JS | [Link](https://github.com/pywebio/PyWebIO) | Practice |\n\n[Back to contents](#contents_prog)\n\n\u003c/details\u003e \n\n\u003cdetails close\u003e\n\u003csummary\u003e \u003cb\u003e Statistics \u003c/b\u003e 📊 \u003c/summary\u003e\n\u003cbr\u003e \n\nComing soon! \n\n\u003c/details\u003e \n\n\u003cdetails close\u003e\n\u003csummary\u003e \u003cb\u003e Econometrics + Causal Inference \u003c/b\u003e 📈 \u003c/summary\u003e\n\u003cbr\u003e \n\n\u003ca name=\"contents_econ\"\u003e\u003c/a\u003e \n## Contents\n\n📌 **OLS regression analysis**\n- [Fundamentals](#fundamentals_py)\n- [OLS Violations](#data_py)\n- [Programming Applications](#program_ols)\n\n📌 **Microeconometrics \u0026 Statistical Models**\n- [Fundamentals](#fundamentals_py)\n- [Some Discrete Choice Models](#discrete)\n- [Maximum Likelihood Estimation](#mle)\n- [Logistic Regressions](#logistic)\n- [Generalised Moment of Methods](#gmm)\n- [Programming Applications](#program_micro)\n\n📌 **Time Series**\n- [Fundamentals: General](#fundamentals_py)\n- [AR Models](#ar)\n- [MA Models](#ma)\n- [ARMA + ARIMA Models](#arma)\n- [VAR Models](#var)\n- [VECM Models](#vecm)\n- [Others](#others_time)\n- [Programming Applications](#program_time)\n\n📌 **Applied Econometrics + Causal Inference**\n- [Fundamentals of Causal Inference](#fundamentals_applied)\n- [Difference in Differences](#diff_in_diff)\n- [Regression Discontinuity Designs](#reg_design)\n- [Instrumental Variables](#instru_var)\n- [Fixed Effects](#fixed)\n- [Causal Machine Learning + Programming Applications](#program_applied)\n\n📌 **Computational Methods in Econometrics**\n- [Fundamentals](#fundamentals_comp_econ)\n\n\u003ca name=\"fundamentals_applied\"\u003e\u003c/a\u003e \n## 🎯 Fundamentals of Causal Inference\n\u003e Causal inference fundamentals including Judea Pearl's work, DAGs, matching, and more!\n\n‣ Books 📚\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| The Mixtape with Scott | A podcast involving discussions with economists, scientists and more. The site also includes sessions on causal inference methods. | [Link](https://causalinf.substack.com/podcast) | Learn |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn |\n| | |  | Practice |\n\n‣ Academic Papers\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn |\n| | |  | Practice |\n\n[Back to contents](#contents_econ)\n\n\u003ca name=\"diff_in_diff\"\u003e\u003c/a\u003e \n## 🎯 Difference in Differences\n\u003e All about the estimating technique, assumptions, violations and more!\n\n‣ Books 📚\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| Paragraph | Text | Link | Practice |\n\n‣ Websites 💻\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn | \n| | Text | | Practice |\n\n‣ Github repos\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn |\n| | |  | Practice |\n\n‣ Academic Papers\n| Name | Description | Link 🔗 | Learn/Practice |\n| --- | ----------- | --- | --- |\n| Header | Title | Link | Learn |\n| Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania | Labour market effects of an increase in the minimum wage in New Jersey in 1992 | [Link](https://davidcard.berkeley.edu/papers/njmin-aer.pdf) | Learn |\n\n[Back to contents](#contents_econ)\n\n..Others coming soon!  \n\n\u003c/details\u003e \n\n### Questions\n\n\u003cdetails close\u003e\n\u003csummary\u003e How can I contribute? \u003c/summary\u003e\n\u003cbr\u003e\n- Any contributions are welcome, as this repo is not exhaustive. \n\u003cbr\u003e \n- If you would like to, please get in touch on Linkedin or email (in my profile homepage: readme or site) or feel free to make a pull request.\n\u003cbr\u003e\n- Don't forget to share this with anyone who might find it useful!\n\u003c/details\u003e\n\n\u003cdetails close\u003e\n\u003csummary\u003e Why these resources/areas in particular? \u003c/summary\u003e\n\u003cbr\u003e\n- The topics interest me and will help me keep track of my progress and learning (and hope this does the same for you as well!) They are also suited for those interested in (academic or professional) careers or topics at the intersection of econometrics, statistics and programming.\n \u003cbr\u003e\n- Resources in the econometrics and statistics sections provide guidance on statistical models, causal inference, time series analysis, and more, making them especially useful for those interested in data science and machine learning. Each of the statistics and econometrics areas has resources on applications to programming as well.\n \u003cbr\u003e\n- Having everything in one place makes it much easier to find resources without having to search through the vast amount of information in various locations (that's probably not organised well too!) You're more likely to delve into an area if you're provided with sufficient details and can find adequate information and resources to get started.\n\u003c/details\u003e\n\n\u003cdetails close\u003e\n\u003csummary\u003e Are these resources suitable for those with a beginner, intermediate, or advanced background? \u003c/summary\u003e\n\u003cbr\u003e\n- The resources are intended to suit individuals with varying backgrounds.    \n \u003cbr\u003e\n- You can get an idea of the levels by reading the descriptions and selecting the relevant links.\n\u003cbr\u003e\n- You're welcome to contribute by adding an additional column to the tables and providing this information!\n\u003c/details\u003e \n\n\u003cdetails close\u003e\n\u003csummary\u003e Additional notes \u003c/summary\u003e\n\u003cbr\u003e\n- Resources with a github repository and website are only included in either of the two sections. \n\u003cbr\u003e \n- All of the resources listed here are intended to be entirely free to use, thereby omitting some popular resources. Please refer to the contribute section if you would like to add anything that's missing.\n\u003cbr\u003e\n- I'm working to add more resources when I find any and get the time to.\n\u003c/details\u003e \n\n---  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv-mayya%2Fprogramming-statistics-and-econometrics-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fv-mayya%2Fprogramming-statistics-and-econometrics-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv-mayya%2Fprogramming-statistics-and-econometrics-resources/lists"}