Plans for second edition
The second edition, planned for early 2025 will focus on correcting errors, improving some explanations and adding minor edits overall. There may be a single new chapter.
Error correction
The most important plan will be correcting typos and errors based on the errata page.
- typos, errors
- improve unclear sentences
- add a few lines of explanation when needed
New things, minor edits
We plan some (not many) new things, typically a few extra paragraphs, maybe a short new section.
Some ideas are
Part 1
| Chapter | Topic | Idea |
|---|---|---|
| 02 | variable naming | Add a few para on naming variables, some ideas and when it’s really important. Extend 2.U1 or add 2.U2 |
| 05 | now short CS | Maybe add a case study on estimating arrival time |
Part 2
| Chapter | Topic | Idea |
|---|---|---|
| 08 | Practice of standard errors | Discuss special cases. One source is Gelman’s JE bit but we had thhought about countlessly. |
| 10? | Frisch-Waughn-Lowell theorem | Add a short section on FWL (no proof), with a case study application. The key application will be a graph, ie show show scatterplot despite controls. Maybe use earnings. Or even add a new case study on Mankiw-Romer-Weil QJE growth regressions. Deepnote |
| 10,21 | dataviz | Add coeffplots |
| 10,22 | p-values | Show tables with p-values and stars, add a para discussion ref back to p-hacking + both have pros and cons |
| 8 | attenuation | Add example for attenuation bias from Feodora Teti customs data paper, real policy implications |
Part 3
| Chapter | Topic | Idea |
|---|---|---|
| 13 | r vs python results | Add a few para/section on discussing that results that are borne out of algos without a close solution, will vary across platforms |
| 13 | loss | Price prediction model trade-offs, loss function Kayak |
| 14 | var imp for OLS | For linear models in prediction, add a few para, new section on variable importance |
| 14 | ln OLS correction | More on what smearing does, when it’s better to use other formula, bias, MAE vs RMSE |
| 14 | Quant reg | If MAE is target, qreg is a way. MAE vs RMSE discussion |
| 14 | Correlated predictors | In any predictive model (OLS, RF), when we have many predictors that are correlated, we have problems: varimp and interpretation. Ideas: PCA, groupings, drop |
| 16 | SHAP for ML | For the machine learning bit, add SHAP in addition to VIP |
| 16 | ensemble for OLS | For linear models in prediction, we can also have an ensemble model, ln+log |
| 16 | cloud comp | Add run time in google colabs / amazon cloud for Table 16.4 |
Part 4
| Chapter | Topic | Idea |
|---|---|---|
| 19 | SUTVA | 2 para – Explicit about SUTVA |
| 21 | More on RDD | A more detailed example on RDD, maybe even a short case study |
| 21 | Good vs bad control | Two example stories with discussion on controls, confounders, mechanism and collider |
| 24 | Add a new DiD | Event study, maybe add one of new DiD method using the same case study |
Feedback
We are open to suggestions! Plase make a suggestion for a minor change or a short addition you think would be helpful HERE. Also report errors, pls.