Economics
Econometrics
8 books for Econometrics, part of the Economics course map, listed from introductory to advanced. 3 of them have an official free edition.
Suggested reading compiled by this site for university students and independent learners. This is not an official university reading plan. Check your own syllabus before buying; editions and exercise numbers differ.
Books in this reading list · 8
- Doing Economics: Empirical Projects
- Mastering 'Metrics: The Path from Cause to Effect
- Using Econometrics: A Practical Guide
- Introduction to Econometrics
- Introductory Econometrics: A Modern Approach
- The Effect: An Introduction to Research Design and Causality
- Causal Inference: The Remix
- Mostly Harmless Econometrics: An Empiricist's Companion
Study guides
Compare three books with short trial tasks, follow a six-step reading route, and test your reasoning with worked examples and changed assumptions.
Reading order
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Doing Economics: Empirical Projects
Read the official free edition ↗Twelve free empirical economics projects for learning to clean data, visualize evidence and investigate policy questions with common software.
Prerequisites: Basic percentages, averages and spreadsheet use are enough to begin. Choose one software track; prior programming is not necessary for the spreadsheet route.
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Mastering 'Metrics: The Path from Cause to Effect
An accessible introduction to the empirical methods economists use to estimate causal effects. Built around five core tools, it explains each through well-known studies of education, health, crime and policy, using plain language, simple notation and memorable examples rather than formal derivations.
Prerequisites: Basic statistics, such as means, variances and a first look at regression, is helpful but not strictly required.
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Using Econometrics: A Practical Guide
An introductory econometrics textbook that teaches single-equation regression through intuition and practice rather than matrix algebra. It concentrates on specifying, estimating and interpreting regression models, on diagnosing common problems in real data, and on the judgement needed to carry out and report a small empirical research project.
Prerequisites: An introductory statistics course is helpful, and readers should be comfortable with algebra; calculus and matrix algebra are not needed.
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Introduction to Econometrics
An applications-led econometrics text linking regression, research validity and time series, with theory available for deeper study.
Prerequisites: Probability, statistics, algebra and introductory economics are useful. Before instrumental variables, distinguish an association, a first-stage change and an exclusion assumption.
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Introductory Econometrics: A Modern Approach
A regression-centered econometrics text spanning cross-section, time-series and advanced applications in the selected 2006 edition.
Prerequisites: Review expectations, variances, regression interpretation and algebra; basic calculus helps with theory. Keep conditional-mean assumptions separate from a coefficient’s numerical estimate.
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The Effect: An Introduction to Research Design and Causality
Read the official free edition ↗A free research-design text that starts with causal questions and identification before introducing estimation tools and implementation.
Prerequisites: Basic statistics and regression help; elementary R, Stata or Python supports implementation. First distinguish a causal target, observed association and assumptions connecting them.
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Causal Inference: The Remix
Read the official free edition ↗An expanded causal-inference guide connecting treatment assignment, target effects and modern panel designs, with a free evolving author version.
Prerequisites: Have a working command of probability, statistical inference and linear regression. Be able to read simulation code and interpret treatment-effect estimates before attempting the more demanding panel chapters.
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Mostly Harmless Econometrics: An Empiricist's Companion
A compact guide to the econometric tools that applied microeconomists actually use for causal questions: regression as a device for statistical control, instrumental variables, differences-in-differences, regression discontinuity and quantile regression. It argues for simple, transparent designs and explains why more elaborate methods are often unnecessary, with worked examples from labour and public economics.
Prerequisites: A first econometrics course covering ordinary least squares, plus probability and statistics at the level of expected values, variances and sampling distributions.