EC 510/410 Economic Data Analysis Using R
Fall 2015, 4:40-6:30pm TTH (CH-307)
Prof. K.-P. Lin (CH 241G, 725-3931)
Office Hours: 3:30-4:30 TTH & by appointment
Economic data observations come in different forms and structures. Data structures such as cross sections,
time series, and panel data are familiar in economics. With the advances of information technology and
rapid growth of data collection in size and scale, "I keep saying that the sexy job in the next 10 years
will be statisticians."
(Hal Varian, Google's Chief Economist).
In this course, we will take a new look at economic data analysis, or econinformatics,
using the powerful open source language R.
This course begins from learning the basics of R to using it to explore and analyze many different types
of economic data. Exploring and visualizing data are requisites for more advanced statistical and
econometric analysis. Then, we learn important skills of data analytics with applications in R.
Knowledge of using a computer for programming is useful but not necessary, as this course is learning by doing in nature.
The course is open to both graduates and senior undergraduates with minimum requirement of basic econ, math/stat, and PC computing experience.
Texts and Software
- Required:
- Recommended:
- Vikram Dayal, An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing, Springer Briefs in Economics, Springer (India), 2015.
- Christian Kleiber and Achim Zeileis, Applied Econometrics with R, Springer-Verlag, New York, 2008.
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, Introduction to Statistical Learning with Applications in R, Springer 2013.
- Roger D. Peng, R Programming for Data Sciences, Learnpub.com, 2015.
- Hal R. Varian, Big Data: New Tricks for Econometrics, Journal of Economic Perspectives 28:2 (3-28), Spring 2014.
Topics
- Introduction to R
- Getting Started - Using RStudio IDE
- Economic Data
- Cross Sections, Time Series, and Panel Data
- High Frequency and Massive Data Sets
- Data Visualization - Using Graphs
- Basic Plots: Scatter, Bar, Line, Box, ...
- Histograms and Distributions
- Data Analysis I
- Descriptive Statistics
- ANOV and Correlation
- Data Analysis II
- Regression and Classification
- Cross Validation and Model Selection
- Case Studies
- The Economist's Big Mac Price Index
(1.1,
1.2,
1.3,
1.4,
1.5,
1.6,
1.7)
- Wine Sales in Vancouver BC
(2.1,
2.2,
2.3,
2.4)
- Longley Data Revisited
(3.1,
3.2,
3.3,
3.4,
3.5,
3.6)
- "I Just Ran Two Million Regressions"
(Data,
4.1,
4.2)
- U.S. Stock Market: SP500 and VIX
(5.1,
5.2)
Lecture Notes
Lecture notes and examples will be available for download during class in progress.
Notes:
1,
2,
3,
4,
5,
6.
Examples:
0.1,
0.2,
0.3,
0.4,
0.5,
1.1,
1.2,
1.3,
1.4,
2.1,
2.2,
3.1,
3.2,
3.3,
3.4,
Homework
Expectation
- There will be a mid-term (November 5, in class) and
a final exam (December 8). In addition, 4 or 5 homework
(possibly accompanied with presentation) will be assigned periodically (due every 2 weeks in average).
- A data project is required for graduate students taking this course EC510.
A one-page project proposal is due on or before November 10 for approval.
Final report of the project is then due on or before December 8.
For assistance in writing a course project or paper, please check out the writing resources and workshops at
PSU Writing Center.
- Grade distribution of this course looks like this:
| EC410 | EC510 |
Mid-Term | 40% | 30% |
Final | 40% | 30% |
Project | | 20% |
Homeworks | 20% | 20% |
Copyright©
Kuan-Pin Lin
(Last updated: 11/19/2015)