I'd like to know which book you would recommend for studying econometrics in a rigorous way at a PhD level.

I'm aware that this question has already been asked, but what I want to question is slightly different. I found the Hamilton book for time series the best one by far. The problem I have with this book is that it lacks in providing examples about coding, maybe because is quite old. Is there any book on the market that is rigorous as the Hamilton and provides coding examples? There is something out there but is more about a statistical approach.

About cross-section analysis, in my opinion, the Wooldridge (advanced version) is the real Bible. Is there, something, also for this research field, that is more about coding and still rigorous as the Wooldridge?


2 Answers 2


I am not aware of any book that is rigorous as well as deals with the programing aspect. That doesn't mean they are complementary. Hamilton deals with the time series from econometrics point of view. It is rigorous but not dry. My go-to books for intermediate to advanced econometrics would be Econometric Analysis by Greene (as mentioned in utobi's answer), Estimation and Inference in Econometrics by Davidson, MacKinnon, Econometrics by Schmidt, Statistical Limit Theory by Davidson, Advanced Econometrics by Amemiya.

With enough digressions, let me recommend you some books that might cater somewhat to your requirements to a certain extent:

$\bullet$ Introduction to Econometrics with R, by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer.

Written by one of our community's prolific users, Christoph Hanck, this book (available online) provides enough insight for a beginner to venture into programming without losing the spirit. They covered everything a standard graduate text would need to: from the classical regression inferences to panel data, instrumental variables. What I liked about this is that it doesn't lower the momentum; rather moves at an even pace with enough intuition. Unfortunately, I won't count it as an advanced book but this could provide an ample scope to explore further with a sound programming mind.

$\bullet$ Applied Econometrics with R by Christian Kleiber, Achim Zeileis, Springer Science$+$Business, $2008$.

This is a short book written with more or less the same intention as that of the former. Again lucidly written explaining each and every component of a script and the graphical outputs, the major highlight would be the chapter on Time Series which deals with structural models, unit roots, cointegration.

$\bullet$ Using R for Introductory Econometrics, by Florian Heiss, $2020$.

Liked Wooldridge? Well you would love this for the author wrote the book based on Wooldridge's text. The author focused on the implementation of $\mathtt{Tidyverse}$, simulations, time series regression, panel data, count data, censoring and truncation.

The author even wrote a companion book for implementation in Python.


A very popular Econometrics textbook is Econometric Analysis by W. Greene, now in its 8th edition, ISBN-13: 9781292231150. It is written in a crystal clear style and is essentially complete in terms of topics coverage. For the computations, Greene seems reluctant to stick to a particular software. Using his words (pag. 39):

A more extensive list of computer software used for econometric analysis can be found at the resource Web site, http://www.oswego.edu/~economic/econsoftware.htm. With only a few exceptions, the computations described in this book can be carried out with any of the packages listed. NLOGIT was used for the computations in most of the applications. This text contains no instruction on using any particular program or language. Many authors have produced RATS, LIMDEP/NLOGIT, EViews, SAS, or Stata code for some of the applications, including, in a few cases, in the documentation for their computer programs. There are also quite a few volumes now specifically devoted to econometrics associated with particular packages, such as Cameron and Trivedi’s (2009) companion to their treatise on microeconometrics


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