<p>Wooldrige - Econometric Analysis of Cross Section and Panel Data</p>

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

CC BY-SA 4.0
2019-07-08T09:05:59.517Z
2019-07-08T09:05:59.517Z
E. Sommer
<p>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.</p>

CC BY-SA 4.0
2019-07-08T15:33:45.903Z
2019-07-08T15:33:45.903Z
Shenan
<ol>
<li>Introductory Econometrics: A modern approach by Wooldridge </li>
<li>Econometric Theory and Methods - Davidson and MacKinnon </li>
<li>Econometric Analysis of Cross Section and Panel Data. by Jeffrey Wooldridge</li>
<li>Mostly Harmless Econometrics </li>
<li>Microeconometrics - Trivedi </li>
</ol>

CC BY-SA 4.0
2019-11-09T05:10:58.777Z
2019-11-09T05:10:58.777Z
Masnun Hosain
<p>Cheng Hsioa's (sp.?) "Analysis of Panel Data".</p>

CC BY-SA 4.0
2019-07-11T09:54:20.033Z
It's a further recommended reading, so it does answer the question. Would be nice however to add the reasons for the recommendation.
Jan Höffler
CC BY-SA 4.0
2019-07-16T12:03:17.100Z
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.
eSurfsnake
CC BY-SA 4.0
2019-07-09T04:01:56.587Z
2019-07-09T04:01:56.587Z
eSurfsnake
<p>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. </p>

CC BY-SA 4.0
2019-07-08T16:17:10.807Z
2019-07-08T16:17:10.807Z
Brennan
<p>Even if you have already covered the greatest part of <em>Basic Econometrics</em> by Gujarati, I think this book is really good because of the quality of explanations</p>
<p>Part III and IV could sound partially new to you and part I and II are great foundations so I recommend it.</p>

CC BY-SA 4.0
2021-06-17T11:33:15.160Z
2021-06-17T11:52:55.017Z
2021-06-17T11:52:55.017Z
Tortar
<p>I would like to add Bruce Hansen's &quot;Econometrics&quot;. It's less detailed than some other sources mentioned here, but very comprehensive in terms of topics it covers and freely available as pdf:
<a href="https://www.ssc.wisc.edu/%7Ebhansen/econometrics/Econometrics.pdf" rel="nofollow noreferrer">https://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf</a></p>
<p>and Scott Cunningham's Causal Inference: The Mixtape, which is in the spirit of MHE and is also freely available at:
<a href="https://mixtape.scunning.com/" rel="nofollow noreferrer">https://mixtape.scunning.com/</a></p>

CC BY-SA 4.0
2021-06-17T09:48:43.047Z
2021-06-17T10:34:04.613Z
2021-06-17T10:34:04.613Z
Papayapap
<p>For a graduate level, but highly accessible, treatment of econometrics, I would recommend Hayashi - <em>Econometrics</em>. It covers some of the topics you mentioned in more detail, and also dives into the (probability) theory behind them. The chapters on GMM and the asymptotic theory of M-estimators are my personal highlights.</p>
<p>For a more practically oriented, highly readable and modern treatment of econometrics from the perspective of causal inference, I recommend Cunningham - <em>Causal Inference: the mixtape</em>. It covers diff-in-diff and regression discontinuity, but also several other methods for estimating causal parameters. It also places these models in either the context of directed acyclical graphs or the potential outcomes framework, so you get a better view of how researchers talk and think about causality.</p>
<p>Finally, if you are interested in time series, I recommend Brockwell and Davis - <em>Introduction to time series and forecasting</em>. This is indeed an introduction, but (depending on your math background) it will take work to understand the material. If you spend some time with it, it pays a lot of dividends in terms of developing a deep understanding of the topic.</p>

CC BY-SA 4.0
2021-06-17T15:40:34.383Z
2021-06-17T15:45:52.853Z
2021-06-17T15:45:52.853Z
Alba
<p>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 <em>causal inference</em> does not look like it was the core of the classes you've taken.</p>

<p>If that's correct, then I would recommend reading:</p>

<ul>
<li>Mastering 'Metrics (<a href="https://rads.stackoverflow.com/amzn/click/com/0691152845" rel="nofollow noreferrer" rel="nofollow noreferrer">https://www.amazon.com/Mastering-Metrics-Path-Cause-Effect/dp/0691152845</a>),</li>
<li>Mostly Harmless Econometrics (<a href="https://www.mostlyharmlesseconometrics.com/" rel="nofollow noreferrer">https://www.mostlyharmlesseconometrics.com/</a>)</li>
</ul>

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

CC BY-SA 4.0
2019-07-08T09:49:35.557Z
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.
E. Sommer
CC BY-SA 4.0
2019-07-08T09:18:02.203Z
2019-11-06T20:07:02.953Z
2019-11-06T20:07:02.953Z
Martin Van der Linden
<p>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:</p>

<ol>
<li>OLS, Multiple OLS, heteroskedasticity</li>
<li>Time series, autocorrelation, ARDL</li>
<li>Pooled model, fixed effects, random effects</li>
<li>IVs, Diff-in-diff, Probit, Tobit</li>
<li>Fuzzy and Sharp RD</li>
</ol>

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

CC BY-SA 4.0
2019-07-11T00:20:20.663Z
you might find this earlier question and answers useful: https://economics.stackexchange.com/questions/12405/books-to-self-study-applied-econometrics/12417#12417
Andrew M
<econometrics><reference-request>
What are some further readings in Econometrics you recommend?
Pablo Derbez
# What are some further readings in Econometrics you recommend?

(Asked by: username_0 on 2019-07-08)

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.

**Tags:** econometrics, reference-request

> **Comments:**
> **username_1**: you might find this earlier question and answers useful: https://economics.stackexchange.com/questions/12405/books-to-self-study-applied-econometrics/12417#12417  

---

## 9 answers

### Answer by username_11 on 2019-07-08. Score: 8 | Accepted answer

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 classes you've taken.

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

* Mastering 'Metrics ([https://www.amazon.com/Mastering-Metrics-Path-Cause-Effect/dp/0691152845](https://rads.stackoverflow.com/amzn/click/com/0691152845)),
* Mostly Harmless Econometrics (<https://www.mostlyharmlesseconometrics.com/>)

before reading anything else (e.g., Wooldridge or Greene, as referenced in username_2's answer).

> **Comments:**
> **username_2**: 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.  

---

### Answer by username_2 on 2019-07-08. Score: 6

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.


---

### Answer by username_3 on 2019-07-08. Score: 1

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.


---

### Answer by username_4 on 2019-11-09. Score: 1

1. Introductory Econometrics: A modern approach by Wooldridge
2. Econometric Theory and Methods - Davidson and MacKinnon
3. Econometric Analysis of Cross Section and Panel Data. by Jeffrey Wooldridge
4. Mostly Harmless Econometrics
5. Microeconometrics - Trivedi


---

### Answer by username_5 on 2019-07-09. Score: 1

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

> **Comments:**
> **username_6**: It's a further recommended reading, so it does answer the question. Would be nice however to add the reasons for the recommendation.  
> **username_5**: 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.  

---

### Answer by username_7 on 2019-07-08. Score: 0

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.


---

### Answer by username_8 on 2021-06-17. Score: 0

Even if you have already covered the greatest part of *Basic Econometrics* by Gujarati, I think this book is really good because of the quality of explanations

Part III and IV could sound partially new to you and part I and II are great foundations so I recommend it.


---

### Answer by username_9 on 2021-06-17. Score: 0

I would like to add Bruce Hansen's "Econometrics". It's less detailed than some other sources mentioned here, but very comprehensive in terms of topics it covers and freely available as pdf:
[https://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf](https://www.ssc.wisc.edu/%7Ebhansen/econometrics/Econometrics.pdf)

and Scott Cunningham's Causal Inference: The Mixtape, which is in the spirit of MHE and is also freely available at:
<https://mixtape.scunning.com/>


---

### Answer by username_10 on 2021-06-17. Score: 0

For a graduate level, but highly accessible, treatment of econometrics, I would recommend Hayashi - *Econometrics*. It covers some of the topics you mentioned in more detail, and also dives into the (probability) theory behind them. The chapters on GMM and the asymptotic theory of M-estimators are my personal highlights.

For a more practically oriented, highly readable and modern treatment of econometrics from the perspective of causal inference, I recommend Cunningham - *Causal Inference: the mixtape*. It covers diff-in-diff and regression discontinuity, but also several other methods for estimating causal parameters. It also places these models in either the context of directed acyclical graphs or the potential outcomes framework, so you get a better view of how researchers talk and think about causality.

Finally, if you are interested in time series, I recommend Brockwell and Davis - *Introduction to time series and forecasting*. This is indeed an introduction, but (depending on your math background) it will take work to understand the material. If you spend some time with it, it pays a lot of dividends in terms of developing a deep understanding of the topic.


---

