凡是搞计量经济的，都关注这个号了
稿件：econometrics666@126.com
所有计量经济圈方法论丛的code程序
, 宏微观数据库和各种软件都放在社群里.欢迎到计量经济圈社群交流访问.
讲义
R 代码、Python Notebooks
实验室材料
高级部分
欢迎来到《数据科学导论课程》。本课程是为期一年的数据科学导论的前半部分。该课程的重点是分析混乱的现实生活数据，使用统计和机器学习方法进行预测。课程分为3个模块，每个模块都将使用数据开展如下五个关键方面的学习：数据采集——数据整理、清洗、采样，得到合适的数据集；
数据管理——快速可靠地访问数据；
探索性数据分析——产生假设和建立直觉；
预测或统计学习；
交流——通过可视化、故事和可解释的总结来总结结果。
课程讲义
Lecture 1: Introduction (Sep. 03, 2019)
Lecture 2: Data and Data Exploration (Sep. 04, 2019)
Lecture 3: Pandas and Web Scraping (Sep. 11, 2019)
Lecture 4: Introduction to Regression (Sep. 16, 2019)
Lecture 5: Linear Regression (Sep. 18, 2019)
Lecture 6: Multiple Linear Regression, Polynomial Regression (Sep. 23, 2019)
Lecture 7: Model Selection and Regularization (Sep. 25, 2019)
Lecture 8: Regularization and EDA (Sep. 30, 2019)
Lecture 9: Visualization for Communication (Oct. 02, 2019)
Lecture 10: Logistic Regression (Oct. 07, 2019)
Lecture 11: Logistic Regression 2 (Oct. 09, 2019)
Lecture 12: KNN Classification & Imputation (Oct. 16, 2019)
Lecture 14: PCA (Oct. 23, 2019)
-
Lecture 15: Decision Trees (Oct. 28, 2019)
Lecture 16: Bagging, & Random Forest (Oct. 30, 2019)
Lecture 17: Boosting Methods (Nov. 04, 2019)
Lecture 18: Neural Networks 1 – Perceptron and MLP (Nov. 06, 2019)
Lecture 19: NN 2: Anatomy of NN, design choices (Nov. 11, 2019)
Lecture 20: NN 3: Back Propagation (Nov. 13, 2019)
Lecture 21: NN 4: Regularization methods (Nov. 18, 2019)
Lecture 22: Visualization for Model Interpretation (Nov. 20, 2019)
Lecture 23: Experimental Design & Testing I (Nov. 25, 2019)
Lecture 24: Experimental Design & Testing II (Dec. 02, 2019)
主题和R、Python代码实操
Activation Function
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
AdaBoost
Adaboost And Xgboost
Array Reshape
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Bagging
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 16: Bagging, & Random Forest
Batching
Bayesian
Beautiful Soup
Beautifulsoup
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Bias
Biases
Big Data
Boosting
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
Lecture 17: Boosting Methods
Lecture 17: Boosting Methods [Notebook]
Bootstrap
Boundaries
Categorical Predictors
Categorical Variables
CI
Classification
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Lecture 12: KNN Classification & Imputation
Lecture 10: Logistic Regression [Notebook]
Collinearity
Communication
Confidence Intervals
-
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lecture 5: Linear Regression
Confusion Matrix
Crawl
Cross-Validation
Lecture 11: Logistic Regression 2 [Notebook]
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Lecture 7: Model Selection and Regularization
CV
Data
Data Cleaning
Data Exploration
Data Science Demo
Data Science Process
Data Scraping
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Dataframe
Decision Boundaries
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Decision Trees
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
Demo
Descriptive Statistics
Dictionaries
Dimensionality Reduction
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
Lab 8: PCA
Lab 8: PCA [Notebook]
Advanced Sections 4: PCA
Lecture 14: PCA
Lecture 14: PCA [Notebook]
Dropout
Eda
Lecture 9: Visualization for Communication
Lecture 8: Regularization and EDA
Lecture 3: Pandas and Web Scraping
Eigenvalues
Eigenvectors
Eignevalues
Elastic Net
Entropy
Lab 9: Decision Trees
Lab 9: Decision Trees [Notebook]
Lecture 15: Decision Trees
Lecture 15: Decision Trees [Notebook]
Explained Variance
Exploratory Data Analysis
Feed Forward
Feed Forward Neural Networks
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Functions
Gini Index
GLM
Google Sites
Gradient Descent
Html
Http
Hypothesis Testing
Lecture 6: Multiple Linear Regression, Polynomial Regression
Advanced Section 1: Linear Algebra and Hypothesis Testing
Lecture 5: Linear Regression
Imputation
Information Gain
Interaction Terms
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lecture 6: Multiple Linear Regression, Polynomial Regression
Introduction
K-Nearest Neighbors (KNN) Regression
Keras
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
KNN
Lecture 12: KNN Classification & Imputation
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
KNN-Classification
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
KNN Imputation Classification
Knn K-Nearest Neighbors (KNN)
KNN Regression
Lasso
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Lecture 8: Regularization and EDA
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Linear Algebra
Linear Regression
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lecture 5: Linear Regression
Lab 3: Scikit-learn for Regression [Notebook]
Lists
Logistic Regression
S-Section 06: PCA and Logistic Regression [Notebook]
S-Section 06: PCA and Logistic Regression
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
Lecture 11: Logistic Regression 2 [Notebook]
Lecture 10: Logistic Regression [Notebook]
Logistics
Matplotlib
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Metrics
Mle
Lab 6: Logistic Regression
Lab 6: Logistic Regression [Notebook]
Lab 6: Logistic Regression [Notebook]
MNIST
Model Selection
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Lecture 7: Model Selection and Regularization
Multiclass
Multilayer Perceptron
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Multinomial Regression
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Multiple Linear Regression
-
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lecture 6: Multiple Linear Regression, Polynomial Regression
Multiple Logistic Regression
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Neural Networks
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
NumPy
Lab 1: Python basics, YAML environments, Numpy
Lab 01: YAML Environments, Python basics, Numpy [Notebook]
OOB
Out Of Bag Error
Overfitting
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lecture 6: Multiple Linear Regression, Polynomial Regression
P-Values
Pairplot
Pandas
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping [Notebook]
S-Section 01: Introduction to Web Scraping
Lab 02: More Pandas [Notebook]
Lab 02: Scraping [Notebook]
Lecture 3: Code Pandas + Beautiful Soup [Notebook]
Lecture 3: Pandas and Web Scraping
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
[Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Pca
Pipeline
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]
S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification
Plots
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Polynomial Regression
S-Section 03: Multiple Linear and Polynomial Regression [Notebook]
S-Section 03: Multiple Linear and Polynomial Regression
Lab 4: Multiple and Polynomial Regression
Lab 04: Multiple and Polynomial Regression [Notebook]
Lab 04: Multiple and Polynomial Regression [Notebook]
Lecture 6: Multiple Linear Regression, Polynomial Regression
Predictors
Principal Components Analysis
Principle Component Analysis
Lab 8: PCA
Lab 8: PCA [Notebook]
Probabilities
Python
Qualitative Predictors
R-Square
R^2
Random Forest
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]
S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost
S-Section 06: Bagging and Random Forest [Notebook]
S-Section 07: Bagging and Random Forest
Lecture 16: Bagging, & Random Forest
Regression
Regression Trees
Regularization
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lecture 11: Logistic Regression 2 [Notebook]
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Requests
Response Variable
RF
Ridge
S-Section 04: Regularization and Model Selection [Notebook]
S-Section 04: Regularization and Model Selection
Advanced Section 2: Regularization
Advanced Sections 2: [Notebook]
Ridge Regression
Roc
Scikit-Learn
Scraping
Seaborn
Lab 5: Exploratory Data Analysis, seaborn, more Plotting
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]
Simple Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Sklearn
Lecture 11: Logistic Regression 2 [Notebook]
Lecture 10: Logistic Regression [Notebook]
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Statistical Model
Statsmodels
-
S-Section 02: kNN and Linear Regression [Notebook]
S-Section 02: kNN and Linear Regression
Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape
Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Lab 03: Prelab [Notebook]
Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]
Stochastic Gradient Descent
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]
S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
T-Test.
Tensorflow
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 12: Building and Regularizing your first Neural Network [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
S-Section 09: Feed forward neural networks [Notebook]
S-Section 09: Feed forward neural networks
The Data Science Process
Train-Test
Training
Training And Testing Data Splitting
Trees
Variable Importance
Variance Vs Bias
Visualization
Web Pages
Web Scraping
Website Scraping
Websites
Weights
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]
Lab 11: Neural Network Basics - Introduction to tf.keras
Wix
Www
YAML
-
Lab 1: Python basics, YAML environments, Numpy
Lab 01: YAML Environments, Python basics, Numpy [Notebook]
在这里学习：https://harvard-iacs.github.io/2019-CS109A/pages/materials.html关于机器学习，参看1.机器学习之KNN分类算法介绍: Stata和R同步实现（附数据和代码），2.机器学习对经济学研究的影响研究进展综述，3.回顾与展望经济学研究中的机器学习，4.最新: 运用机器学习和合成控制法研究武汉封城对空气污染和健康的影响! 5.Top, 机器学习是一种应用的计量经济学方法, 不懂将来面临淘汰危险！6.Top前沿: 农业和应用经济学中的机器学习, 其与计量经济学的比较, 不读不懂你就out了！7.
前沿: 机器学习在金融和能源经济领域的应用分类总结，8.机器学习方法出现在AER, JPE, QJE等顶刊上了！9.机器学习第一书, 数据挖掘, 推理和预测，10.从线性回归到机器学习, 一张图帮你文献综述，11.11种与机器学习相关的多元变量分析方法汇总，12.机器学习和大数据计量经济学, 你必须阅读一下这篇，13.机器学习与Econometrics的书籍推荐, 值得拥有的经典，14.机器学习在微观计量的应用最新趋势: 大数据和因果推断，15.R语言函数最全总结, 机器学习从这里出发，16.机器学习在微观计量的应用最新趋势: 回归模型，17.
机器学习对计量经济学的影响, AEA年会独家报道，18.回归、分类与聚类：三大方向剖解机器学习算法的优缺点（附Python和R实现），19.关于机器学习的领悟与反思，20.机器学习，可异于数理统计，21.前沿: 比特币, 多少罪恶假汝之手? 机器学习测算加密货币资助的非法活动金额! 22.利用机器学习进行实证资产定价, 金融投资的前沿科学技术! 23.全面比较和概述运用机器学习模型进行时间序列预测的方法优劣！24.用合成控制法, 机器学习和面板数据模型开展政策评估的论文！25.更精确的因果效应识别: 基于机器学习的视角，26.一本最新因果推断书籍, 包括了机器学习因果推断方法, 学习主流和前沿方法
，27.如何用机器学习在中国股市赚钱呢? 顶刊文章告诉你方法！28.机器学习和经济学, 技术革命正在改变经济社会和学术研究，29.世界计量经济学院士新作“大数据和机器学习对计量建模与统计推断的挑战与机遇”，30.机器学习已经与政策评估方法, 例如事件研究法结合起来识别政策因果效应了！31.重磅! 汉森教授又修订了风靡世界的“计量经济学”教材, 为博士生们增加了DID, RDD, 机器学习等全新内容！32.几张有趣的图片, 各种类型的经济学, 机器学习, 科学论文像什么样子？33.机器学习已经用于微观数据调查和构建指标了, 比较前沿！34.两诺奖得主谈计量经济学发展进化, 机器学习的影响, 如何合作推动新想法！35.前沿, 双重机器学习方法DML用于因果推断, 实现它的code是什么？
下面这些短链接文章属于合集，可以收藏起来阅读，不然以后都找不到了。
2.5年，计量经济圈近1000篇不重类计量文章，
可直接在公众号菜单栏搜索任何计量相关问题,
Econometrics Circle
计量经济圈组织了一个计量社群，有如下特征：热情互助最多、前沿趋势最多、社科资料最多、社科数据最多、科研牛人最多、海外名校最多。因此，建议积极进取和有强烈研习激情的中青年学者到社群交流探讨，始终坚信优秀是通过感染优秀而互相成就彼此的。
