<p>The following quote might also be useful. The following is quoted from <em>Mostly Harmless Econometrics</em>, by Angrist and Pischke</p>

<blockquote>
  <p>Two things distinguish the discipline of Econometrics from our older
  sister field of Statistics. One is a lack of shyness about causality.
  Causal inference has always been the name of the game in applied
  econometrics. Statistician Paul Holland (1986) cautions that there can
  be no causation without manipulation, a maxim that would seem to
  rule out causal inference from non-experimental data. Less thoughtful
  observers fall back on the truism that correlation is not causality.
  Like most people who work with data for a living, we believe that
  correlation can sometimes provide pretty good evidence of a causal
  relation, even when the variable of interest has not been manipulated
  by a researcher or experimenter. </p>
  
  <p>The second thing that distinguishes us from most statisticians and
  indeed most other social scientists is an arsenal of statistical
  tools that grew out of early econometric research on the problem of
  how to estimate the parameters in a system of linear simultaneous
  equations. The most powerful weapon in this arsenal is the method of
  Instrumental Variables (IV), the subject of this chapter. As it turns
  out, IV does more than allow us to consistently estimate the
  parameters in a system of simultaneous equations, though it allows us
  to do that as well.</p>
</blockquote>

<p>However, this is not to say that causal inference belongs only to econometrics.
Wasserman, in <em>All of Statistics,</em> remarks at how parallel developments were made within statistics and econometrics,</p>

<blockquote>
  <p>The use of potential outcomes to clarify causation is due mainly to
  Jerzy Neyman and Don Rubin. Later developments are due to Jamie
  Robins, Paul Rosenbaum and others. A parallel devel- opment took place
  in econometrics by various people including Jim Heckman and Charles
  Manski. </p>
</blockquote>

CC BY-SA 3.0
2016-06-17T21:57:17.963Z
2016-06-17T21:57:17.963Z
jmbejara
<p>An interesting question that leads to a debate among econometricians. Some consider that </p>

<p><strong>Econometrics is just statistics applied to economic problems</strong></p>

<blockquote>
  <p>Econometrics is just statistics applied to economic problems—nothing
  more and nothing less. We should probably call it “statistical
  economics,” but I guess people feel that the term “econometrics” has a
  better ring to it. The only cost of using the term “econometrics” is
  that we are sometimes fooled into thinking that we work on a distinct
  discipline, separate from statistics. This is not true. </p>
</blockquote>

<p>From John Stachurski at ANU (see his notes p.108 in Econometric Theory <a href="http://quant-econ.net/_downloads/course_notes.pdf">freely downloadable</a>)</p>

<p>Others consider that</p>

<p><strong>Eonometrics by no means the same as economic statistics</strong></p>

<p>This quote is from <a href="https://en.wikipedia.org/wiki/Ragnar_Frisch">Ragnar Frisch</a> (one of the founders of the Econometric Society) and his <a href="http://www.jstor.org/stable/1912224?seq=2#page_scan_tab_contents">Editor's Note</a> on why the Society has decided to establish its own journal, <a href="https://www.econometricsociety.org/publications/econometrica/browse">Econometrica</a>.</p>

<p>The key idea is that <strong>economic theory</strong> is crucial to understand measurement. Neither theory nor measurement on its own is sufficient to further our understanding of economic phenomena. We need both and measurement without theory is unlikely to provide a satisfactory explanation of the way economic forces interact with each other.</p>

<p>Another difference is, as economists, we are biased towards establishing <strong>causal relationships</strong>. So, this explains our focus on endogeneity issues and strategies of identification.</p>

CC BY-SA 3.0
2016-06-16T17:31:20.710Z
2016-06-16T17:48:49.923Z
2016-06-16T17:48:49.923Z
emeryville
<p>According to Wikipedia, <em>Econometrics is the application of mathematics, statistical methods, and computer science to economic data and is described as the branch of economics that aims to give empirical content to economic relations</em>. It <em>sifts through mountains of data to extract simple relationships</em>. Generally for positions like Analytic Consultant, Data Scientist, Statistician, Quantitative Analyst, background in maths, stats or econometrics is required. What separates econometricians in terms of what they learn in school and how they apply acquired knowledge in work? Or do these fields overlap so much that there is not much to distinguish?</p>

CC BY-SA 3.0
2016-06-17T20:13:54.367Z
You may find this question on Cross Validated SE helpful: http://stats.stackexchange.com/questions/27662/what-are-the-major-philosophical-methodological-and-terminological-differences/27693#27693
Adam Bailey
CC BY-SA 3.0
2016-06-18T00:15:06.933Z
Someone who sins in the basement?
Graeme Walsh
<econometrics><definition>
What is an econometrician?
rsl
# What is an econometrician?

(Asked by: username_0 on 2016-06-16)

According to Wikipedia, *Econometrics is the application of mathematics, statistical methods, and computer science to economic data and is described as the branch of economics that aims to give empirical content to economic relations*. It *sifts through mountains of data to extract simple relationships*. Generally for positions like Analytic Consultant, Data Scientist, Statistician, Quantitative Analyst, background in maths, stats or econometrics is required. What separates econometricians in terms of what they learn in school and how they apply acquired knowledge in work? Or do these fields overlap so much that there is not much to distinguish?

**Tags:** econometrics, definition

> **Comments:**
> **username_1**: You may find this question on Cross Validated SE helpful: http://stats.stackexchange.com/questions/27662/what-are-the-major-philosophical-methodological-and-terminological-differences/27693#27693  
> **username_2**: Someone who sins in the basement?  

---

## 2 answers

### Answer by username_4 on 2016-06-16. Score: 8 | Accepted answer

An interesting question that leads to a debate among econometricians. Some consider that

**Econometrics is just statistics applied to economic problems**

> Econometrics is just statistics applied to economic problems—nothing
> more and nothing less. We should probably call it “statistical
> economics,” but I guess people feel that the term “econometrics” has a
> better ring to it. The only cost of using the term “econometrics” is
> that we are sometimes fooled into thinking that we work on a distinct
> discipline, separate from statistics. This is not true.

From John Stachurski at ANU (see his notes p.108 in Econometric Theory [freely downloadable](http://quant-econ.net/_downloads/course_notes.pdf))

Others consider that

**Eonometrics by no means the same as economic statistics**

This quote is from [Ragnar Frisch](https://en.wikipedia.org/wiki/Ragnar_Frisch) (one of the founders of the Econometric Society) and his [Editor's Note](http://www.jstor.org/stable/1912224?seq=2#page_scan_tab_contents) on why the Society has decided to establish its own journal, [Econometrica](https://www.econometricsociety.org/publications/econometrica/browse).

The key idea is that **economic theory** is crucial to understand measurement. Neither theory nor measurement on its own is sufficient to further our understanding of economic phenomena. We need both and measurement without theory is unlikely to provide a satisfactory explanation of the way economic forces interact with each other.

Another difference is, as economists, we are biased towards establishing **causal relationships**. So, this explains our focus on endogeneity issues and strategies of identification.


---

### Answer by username_3 on 2016-06-17. Score: 2

The following quote might also be useful. The following is quoted from *Mostly Harmless Econometrics*, by Angrist and Pischke

> Two things distinguish the discipline of Econometrics from our older
> sister field of Statistics. One is a lack of shyness about causality.
> Causal inference has always been the name of the game in applied
> econometrics. Statistician Paul Holland (1986) cautions that there can
> be no causation without manipulation, a maxim that would seem to
> rule out causal inference from non-experimental data. Less thoughtful
> observers fall back on the truism that correlation is not causality.
> Like most people who work with data for a living, we believe that
> correlation can sometimes provide pretty good evidence of a causal
> relation, even when the variable of interest has not been manipulated
> by a researcher or experimenter.
>
> The second thing that distinguishes us from most statisticians and
> indeed most other social scientists is an arsenal of statistical
> tools that grew out of early econometric research on the problem of
> how to estimate the parameters in a system of linear simultaneous
> equations. The most powerful weapon in this arsenal is the method of
> Instrumental Variables (IV), the subject of this chapter. As it turns
> out, IV does more than allow us to consistently estimate the
> parameters in a system of simultaneous equations, though it allows us
> to do that as well.

However, this is not to say that causal inference belongs only to econometrics.
Wasserman, in *All of Statistics,* remarks at how parallel developments were made within statistics and econometrics,

> The use of potential outcomes to clarify causation is due mainly to
> Jerzy Neyman and Don Rubin. Later developments are due to Jamie
> Robins, Paul Rosenbaum and others. A parallel devel- opment took place
> in econometrics by various people including Jim Heckman and Charles
> Manski.


---

