资料:经济系博一课程和编程

I. 北美经济系博士一年级的训练应该都很标准,基本上是两学期上完六门三高(微观/宏观/计量 I&II)。第一年结束后是资格考,然后第二年才开始进入各类fields课程。此处列下一些当时整理的资料:

Micro

J. Levin’s Micro Notes

Macro

1.General Notes: Dirk Krueger’s Notes (2012) & Per Krusell’s Notes (2014)

2. Violante’s Heterogeneity in Macroeconomics (2014 Spring)

Econometrics

Hensen’s Econometrics Notes (2020)

II. 第一年之后可能会有用的工具:

Structural Model

1. Discrete Choice Methods with Simulation, by Kenneth Train

2. Practical Methods for Estimation of Dynamic Discrete Choice Models, by Arcidiacono and Ellickson

Big Data & ML

1. Matt Taddy’s Big Data Course

2.Thorsten Joachims’s Counterfactual Machine Learning

3. Benjamin Soltoff’s Computing for the Social Science

4. Econ-ML 

5. AEA: Machine Learning and Econometrics (Susan Athey, Guido Imbens)

6. “Machinistas meet randomistas: useful ML tools for empirical researchers” by Esther Duflo at NBER SI 2018

III. 还有一些编程的资料:

Python

1. Learn Python 3 the Hard Way

2. 廖雪峰的Python 3 教程

3. Python Data Science Handbook

R

1.  R in Action (Book)

2. Hadley Wickham’s R for Data Science

3. Hadley Wickham’s Advanced R  with a recommended Solutions

4. R Reference Card 2.0

5. Geocomputation with R

6. Efficient R programming (2017) by Gillespie & Lovelace

7. Text Mining with R: A Tidy Approach

LaTeX

1. The Not So Short Introduction To LaTeX (Chinese Version, 2017)

2.Wikibooks: LaTeX or Presentation

3. John C Frain’s Applied LATEX for Economists, Social Scientists and Others (2014)

GIS

1.MIT GIS Workshop

2. R as GIS for Economist by Taro Mieno

3. QGIS Training Manual

4.QGIS Tutorials and Tips

Stata

1. Stata Coding Guide by Julian Reif

2. Stata for very large datasets

3. NP Packages

HTML & CSS

Learn to code HTML & CSS by Shay Howe

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