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I've reached the end of my Econometrics courses for the undegraduate level at my university, but I would like to continue learning. I hope I could get some recommendations for further reading. I present a summary (off the top of my head) of what has already been covered in my courses:

  1. OLS, Multiple OLS, heteroskedasticity
  2. Time series, autocorrelation, ARDL
  3. Pooled model, fixed effects, random effects
  4. IVs, Diff-in-diff, Probit, Tobit
  5. Fuzzy and Sharp RD

As well as the relevant tests for each case. I'm also familiar with Econometric theory on matrix form.

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I might be wrong but from what you write, it seems you've been given a "classical" introduction to econometrics: You've covered IVs and Diff-in-diff but apparently only in passing, and causal inference does not look like it was the core of the class you've taken.

If that's correct, then I would recommend reading:

before reading anything else (e.g., Wooldridge or Greene, as referenced in E.Sommer's answer).

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    $\begingroup$ I agree that modern empirical work is less about using fancy estimators but to search for exogenous variation in the real world and exploit it. The main challenge there is to find the appropriate data and to exclude any confounding variation. The estimates itself are usually pretty simple, i.e. standard OLS or Panel Fixed Effects. $\endgroup$ – E. Sommer Jul 8 at 9:49
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Wooldrige - Econometric Analysis of Cross Section and Panel Data

Greene - Econometric Analysis, 8th Edition. This is probably the 'bible', i.e. it covers everything, but I find it hard to digest.

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What types of software have you used? Seeing as you have a pretty good understanding of core econometric principles, the next step is to at least get a few packages on your tool belt. I imagine you have at been exposed to at least one, but having knowledge in multiple languages is very valuable to employers or to an academic career. I suggest having either R or Stata or both. Preferably both as each have their advantages and make any consecutive languages easier to learn.

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Cheng Hsioa's (sp.?) "Analysis of Panel Data".

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    $\begingroup$ It's a further recommended reading, so it does answer the question. Would be nice however to add the reasons for the recommendation. $\endgroup$ – Jan Höffler Jul 11 at 9:54
  • $\begingroup$ Usually we use either (1) cross-sectional data or (2) time series data. Hsiao's book is the classic on how to wring more juice from the fruit when you have concurrent cross-sectional and time-series data. And, it's really well written. $\endgroup$ – eSurfsnake Jul 16 at 12:03
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Not sure about the other fields, but for time series, I would recommend Time Series Analysis 1st Edition by James Douglas Hamilton. This is the book I used to learn time series when I was a graduate student.

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