This section contains resources and links to outside miscellaneous materials that I found useful when doing research or programming.
Links in the External Programming Resources section go to sites and blog posts that do not belong to me. Links in Workflow section go to the official sites for the corresponding apps.
You can find my teaching materials in the Teaching page. For a list of teaching and research assistantship experiences, please see the CV section.
Please let me know if any links are dead/outdated. You can email me at email@example.com.
I only post my discussion slides here. Please contact the related authors directly for the paper or the presentation slides, shown in reverse chronological order.
4th Productivity Research Network Conference: Which Firms Get Credit? Evidence from Firm-Level Data by Gabriela Araujo and Jonathan Hambur
4th Productivity Research Network Conference: Industry- and State-Level Value Added and Productivity Decompositions by Shipei Zeng, Stephanie Parsons, Erwin Diewert, and Kevin Fox
SFS Cavalcade Asia Pacific: Credit Risk Propagation along Supply Chains: Evidence from the CDS Market by Senay Agca, Volodymyr Babich, John Birge, Jing Wu
2nd Productivity Research Network Conference: The Redistributive Role of Automation by Giorgio Presidente.
2nd Productivity Research Network Conference: Is Informal Credit Supplement or Complementary for Financing SME's Investment During the Crisis? Evidence from Vietnam by Long Q Trinh & Peter J Morgan.
SMU Finance Summer Camp: Sentiment, Limited Attention and Mispricing by Xinrui Duan, LiGuo, Frank Weikai Li, Jun Tu.
NTU Finance Conference: Regulation of Charlatans in High-Skill Professions by Jonathan Berk & Jules van Binsbergen.
29th NBER-EASE: Corruption, Political Stability and Efficiency of Government Expenditure on Health Care – Evidence from Asian Countries by Nobuo Akai & Zhenyu Cui.
RMI Conference: Industry Competition, Credit Spreads, and Levered Equity Returns by Alexandre Corhay.
Chicago Booth PhD Student Website Template: This links to my BitBucket repository that contains instructions and sample files to set up a website, following the template and color scheme of Chicago Booth. The site scales with screens of different sizes.
Optimal Trading Execution Code for Minimizing Execution Cost: This script contains the functions for the base (parametric with closed form solutions) version of the optimal trading execution according to Bertsimas and Lo (1998). It shows how optimal trading should depend on how aggressive other market participants are, how noisy the fundamental information is, and other fundamental parameters. All parameters are defined according to the original paper.
Script downloading Useful Packages in R.
External Programming Resources
These range from basic to intermediate R, and do not belong to me.
R Tidyverse Style Guide provides great guidelines, but I also adopt the deviations from Google's style guide (fork of the Tidyverse Guide).
Why R?: Great slide deck on the power of R with great graphics and visualizations written in R.
New to R? Try Swirl! : This website contains lots of useful tutorials.
R: Intro to data.table Package: Once you get past the learning curve, you will not go back to base R.
R: The apply function family: R users should know about this. However, to apply functions by groups (rather than by rows), use data.table or dplyr.
R: Example plots using ggplot2 with code: Shows some of the capabilities of ggplot.
R: ggplot2: slides by Hadley Wickham.
R: ggplot2 Visualization Gallery: Useful examples for inspiration.
R: ggplot2 Extension Visualization Gallery: Useful examples for inspiration using extensions to ggplot2.
R: Benchmarking data.table vs. pandas vs. dplyr: I have gotten into lots of discussion about whether python or R is faster. The answer: It doesn't matter much for small data, but data.table seems to win for larger data sets.
R: 11 Tips to Handle Big Data: A short, incomplete list but useful nonetheless. Turns out R isn't great at handling big data (where big data:= data > 1 TB).
R for Big Data: A cool mindmap of useful R packages for managing and analyzing big data. I would personally add ggplot2 into the Visualization category.
R: Compressing Data Files to Save Space: This saved me an unbelievable amount of space when working with TAQ data.
R: Stargazer Cheat-sheet: In case you need to customize output.
R: Tips with data.table: Fairly advanced looping and editing data in memory.
R: Different file formats: A blog covering rds, feather, fst file formats for fast read/write and interoperability between R and Python.
R: Visualization from VIS 2017 Conference: Good examples of data visualization with R for inspiration.
Data Handling: LocustDB > Clickhouse. Also an overview of Online Analytical Processing (OLAP).
Primer on Advanced Text Analysis: Guide on considering different text similarity measures that can take into account the semantics and grammar in addition to simple word similarity.
Visualizing Transition Matrices: Snippet showing how to visualize transitions with different features. Does not use graph theory.
R: What They Forgot (wtf): For intermediate R users who are mostly self-taught. More advanced topics like code maintenance, library maintenance, version maintenance.
Mendeley: Having used it since I started grad school, I can't imagine what a pain it must be to maintain a huge library of PDF's without this organizer.
LyX: For doing all things LaTeX (although more recently I tend to use PowerPoint more for slides).
Slack: For an organised chat group where we can share files.
Asana Project Management: I learned about this from Matt Gentzkow and Jesse Shapiro's Research guide. This has been immensely useful for collaboration. The integration with Google Calendar is particularly useful for me. However, like Slack, I feel this works better for more stable research/co-author teams.
BitBucket: For writing and code documentation, particularly for team collaboration. Also useful for storing different iterations of output.
Really random stuff that are nice short breaks from work.
Online Textbook on Data Visualization: by Claus Wilke, with everything built in R and source code available through GitHub.
Tips for Economists: A collection of tips from various great sources all collected in one place.
Language-Agnostic Coding Advice for Economists by Ljubica Ristovska, from a presentation at the Harvard Economics Professional Development workshop. Spring 2019.
Econometrics Resources for R (and Stata) from Nick Huntington-Klein at California StateUniversity. The Causal Inference Animated Plots are particularly cool.
A very useful, free Econometrics Textbook by Scott Cunningham at Baylor University. Comes with a Spotify playlist of accompanying rap!
Open Source Etiquette: Best be nice; especially when help is needed.
R for Data Journalism: Useful packages, examples, and tips to integrate data into articles.
A Bunch of Spurious Correlations: Do you find yourself coming up with stories for them?
Typing Test - Check Your Speed!: I found out I type around 132 words per minute (based on the Aesop's fable test). Comes in handy.