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Econometrics is about evidence, not just equations
Regression formulas matter, but the deeper point of the course is learning how data can support or weaken an economic claim.
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01:220:322 · Economics · School 01 — New Brunswick School of Arts and Sciences
Econometrics is Rutgers' introduction to using statistical methods to estimate, test, and predict economic relationships. The official course description emphasizes ordinary least squares regression and the practical complications that come with applying it, while the sample syllabus makes clear that the course usually moves from statistical review into linear regression, model assessment, panel data, binary outcomes, instrumental variables, and quasi-experimental reasoning. The same syllabus also notes regular work with R, so the class is not only about formulas but about using data tools to make and evaluate empirical economic claims.
Can I take it?
Intro To Microeconom · Introduction To Macroeconomics · Statistics I
How hard?
Students rate teaching
SIRS · 1,444 responses · 2014–2025
Spring 2027
3 open in snapshot · 3 instructors
When it meets
College Avenue
Checked-in sections · current seat counts are unavailable.
MW 3:50 PM · College Avenue · 2 sections · 2 open
TTh 2:00 PM · College Avenue · 1 section · 0 open
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The syllabus starts by reviewing statistical concepts before introducing regression. That opening matters because the class assumes students can already think in terms of random variables, expectations, variance, and probability, then asks them to use those tools to reason about real economic data rather than ideal textbook examples.
The first major modeling block is the simple linear regression model. Students learn how to estimate a relationship, interpret coefficients, and think about the difference between fitting a line and claiming something meaningful about an economic relationship.
The course then expands beyond one-variable explanations. This is where students learn how controls, functional form, and nonlinear effects change interpretation, and where regression starts to feel more like a language for organizing evidence than a one-formula procedure.
After estimating models, students need to judge whether the model is trustworthy. The sample syllabus explicitly includes assessing regression models, which usually means checking fit, standard errors, assumptions, and whether a statistically clean result also makes economic sense.
The later part of the course broadens the toolkit beyond basic OLS. Rutgers' sample syllabus lists panel data, binary dependent-variable models, instrumental variables, experiments and quasi-experiments, and possibly prediction with many regressors, which together frame econometrics as a method for causal inference under imperfect real-world conditions.
Public Rutgers discussion consistently treats Econometrics as one of the tougher economics core classes, especially for students who are only loosely comfortable with statistics. At the same time, students often describe it as one of the most useful courses in the major because it turns abstract economics into data analysis, software work, and evidence-based argument.
Regression formulas matter, but the deeper point of the course is learning how data can support or weaken an economic claim.
Ordinary least squares provides the basic framework, but later topics show why real data often needs richer tools and stronger identification logic.
A coefficient can be precisely estimated without being substantively large, and it can be economically interesting even when interpretation needs caution.
The course becomes much more useful once students stop asking only whether a regression ran and start asking whether the identifying assumptions are believable.
Common pitfall
Instead
Translate every coefficient, error term, and test result back into plain-language economic meaning before deciding what the model shows.
Common pitfall
Instead
Refresh probability, variance, expectation, sampling, and inference early, because econometrics builds on them rather than reteaching them slowly.
Common pitfall
Instead
Ask what assumptions make the estimate interpretable and whether omitted variables, selection, or reverse causality could still be doing the work.
Common pitfall
Instead
Use R or whichever package your section emphasizes as a way to test conceptual understanding, not as a button-clicking task detached from the model.
3 open · College Avenue
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114648 | Open | Ruonan Xu | Monday 3:50 PM-5:10 PM at MU 213; Wednesday 3:50 PM-5:10 PM at MU 213MU 213 | College Avenue |
| 0214649 | Open | Landon-Lane | Monday 12:10 PM-1:30 PM at MU 212; Thursday 12:10 PM-1:30 PM at MU 212MU 212 | College Avenue |
| 0314650 | Closed | Blanco Fernande | Tuesday 2:00 PM-3:20 PM at MU 210; Thursday 2:00 PM-3:20 PM at MU 210MU 210 | College Avenue |
| 0414651 | Open | Ruonan Xu | Monday 2:00 PM-3:20 PM at MU 211; Wednesday 2:00 PM-3:20 PM at MU 211MU 211 | College Avenue |
Historical student surveys
Teaching
3.62
Course quality
3.58
Response rate
39.5%
Coverage
60 offerings · 2014–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Walter Paczkowski | 10 | 3.24 | 3.27 |
| Yuan Liao | 6 | 3.70 | 3.65 |
| Ruonan Xu | 6 | 3.47 | 3.50 |
| Liao Yuan | 4 | 3.65 | 3.73 |
| Xiye Yang | 4 | 3.63 | 3.55 |
| Lee Sungkyung | 4 | 3.58 | 3.47 |
Catalog and planning context
Credits
3
Current campuses
College Avenue
Current availability
3 open of 4
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
6
(01:220:102 and 01:220:103 and 01:960:211 and 01:640:135) <em> OR </em> (01:220:102 and 01:220:103 and 01:960:211 and 01:640:151) <em> OR </em> (01:220:102 and 01:220:103 and 01:960:285 and 01:640:135) <em> OR </em> (01:220:102 and 01:220:103 and 01:960:285 and 01:640:151) <em> OR </em> (01:220:102 and 01:220:103 and 01:960:211 and 01:640:130) <em> OR </em> (01:220:102 and 01:220:103 and 01:960:285 and 01:640:130)
No direct degree-list membership appears in the checked-in requirement index.