FAQ for instructors

Graduate economics, applied economics

Q: How does the book cover traditional topics in Econometrics courses?

A. It covers a great of material taught in Econometrics courses, but with a strong application rather than theory and academic research focus.

  1. Part I covers some standard topics like descriptive statistics or corelation, but also some more advanced ones like bootstrap sampling. It also discusses typically missed issues like data collection and vizualization.
  2. Part II is the closest to an applied Econometrics I course material covering regression analysis from OLS to time series.
  3. Part III with prediction is typically omitted.
  4. Part IV has topics from an Econometrics II course like diff-in-diffs, or panel methods.

Q: What is the main advantage of this book over existing Econometrics textbooks?

A. There are several differences, reasons we wrote the book.

  1. Coverage of topics that are essential for an applied data analyst (data vizualization, precise interpretation, a prediction toolset), but often neglegted.
  2. Plain language, with algebra used only when needed, and derivations relegated to appendix for advanced readers.
  3. Case study focus - case studies are 1/3 of the text, and not just toy examples. Real life cases, with data and code shared
  4. Coding language neutral, code shared in Python, R and Stata.
  5. Machine learning methods explained for students with no computer science background
  6. Prepares students for many different career opportunities with transferable skills on data cleaning to modelling and making decisions based on analytics.

Q: I want to have a great deal of theory in my class, how could I integrate the book with a traditional econometrics course

A. There are several differences, reasons we wrote the book.

Undergraduate economics, social science programs

Q: What are the **prerequisites for an undergraduate data analytics course which may be cross-listed with computer science and/or business? **

A. Not much:

  1. Some undergraduate statistics is helpful but not necessary
  2. Math beyond high-school is helpful but not necessary

But along the course intro to coding in R, Stata or Python will be needed

Graduate / Master programs

Q: How much time is needed to go through the textbook fully?

A. One way to proceed is to tackle a chapter a week - so 24 weeks. It may take on average about 4 hours to go through a chapter including theory, the case studies and some discussion. This would mean about 100 contact hours, excluding specific coding seminars.

Students will spend at least 300 minutes with review, practive questions and reviewing code.

Q: How would a coding class fit in?

A. It is useful to have some coding course running in parallel with Data Analysis, especially in the beginning.

In our experience, a coding course of 20-30 hours is useful. This could be done separately or as seminars to the main course. Codes for case studies are becoming more and more complicated, so may be used to learn.

MS Business / Management, MBA

Q: How is this book different to ‘business statistics’, can it be used in those classes?

A. The topics covered in the first half of the book – descriptive statistics, testing, regression analysis – often feature in business statistics classses. Those may be used directly. We add two important factors:

  1. Completed case studies based on real life problems. They illustrate key decision points, the difficulties of working with real life data and potential benefits as well.
  2. Topics that may not be part of business statistics but are useful. Data collection and data quality are main issues in modern analytics. Understanding how we generalize patterns, or model interpretation are both very helpful for future managers, too.


Q: I am running a metrics course for a Phd program for management and finance students in a business school. I plan to do an applied course, slightly less technical than a graduate econometrics course. Is this book the right fit?

A. We think so, because:

  1. It offers a good mix of tools for future applied researchers
  2. It has case studies for finance and management
  3. It has a good introduction and overview of machine learning tool, which has become essential part of toolset in management and finance research, too.
  4. In regards to causal inference, it has a string focus on tools that useful in academia and are widely used, such as panel data or event studies
  5. It has specific chapters on data collection and experiements - two key practical issues for researchers in these fields

Introductory chapters may be skipped, while advanced methods may be needed (e.g. for finance: more time series, such as Garch models, for management: some unsupervised learning, such as factor analyis)