├── eco517_l1.pdf ├── eco517_l10.pdf ├── eco517_l11.pdf ├── eco517_l2.pdf ├── eco517_l3.pdf ├── eco517_l4.pdf ├── eco517_l5.pdf ├── eco517_l6.pdf ├── eco517_l7.pdf ├── eco517_l8.pdf ├── eco517_l9.pdf └── readme.md /eco517_l1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l1.pdf -------------------------------------------------------------------------------- /eco517_l10.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l10.pdf -------------------------------------------------------------------------------- /eco517_l11.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l11.pdf -------------------------------------------------------------------------------- /eco517_l2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l2.pdf -------------------------------------------------------------------------------- /eco517_l3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l3.pdf -------------------------------------------------------------------------------- /eco517_l4.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l4.pdf -------------------------------------------------------------------------------- /eco517_l5.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l5.pdf -------------------------------------------------------------------------------- /eco517_l6.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l6.pdf -------------------------------------------------------------------------------- /eco517_l7.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l7.pdf -------------------------------------------------------------------------------- /eco517_l8.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l8.pdf -------------------------------------------------------------------------------- /eco517_l9.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kolesarm/517/HEAD/eco517_l9.pdf -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # ECO517: Econometric Theory I: Overview 2 | 3 | This half-course constitutes the first six weeks in a first-year graduate 4 | econometric sequence. After reviewing basic probability concepts, it provides 5 | the basic tools for large-sample analysis in econometrics, and sets up a 6 | decision-theoretic framework that allows us to think about estimation and 7 | testing in a rigorous way. You will use these tools throughout the remainder of 8 | the first year and beyond. We also cover some foundational results that are 9 | important in their own right, such as the complete class theorem. 10 | 11 | For Princeton students, homework and solutions to it will be posted on Canvas. 12 | Official course description is at the [Registrar's website](https://registrar.princeton.edu/course-offerings/course-details?term=1252&courseid=001446). 13 | 14 | # Notes 15 | 16 | This repository provides detailed lecture notes with references. They aim to be 17 | self-contained, so they can also serve as a reference in your later graduate 18 | work. As such, a textbook is not needed to survive this course. If your course 19 | goal is more ambitious than that, it is useful to buy one a the three textbooks 20 | listed in the syllabus so that you can get a different perspective on the 21 | material covered. 22 | 23 | # Topics 24 | 25 | 1. Probability review: probability spaces, random variables, transformations of 26 | random variables, quantiles, expectations, independence, covariance, the 27 | multivariate normal distribution [Notes](eco517_l1.pdf) 28 | 2. Convergence, law of large numbers, central limit theorem, delta method 29 | [Notes](eco517_l2.pdf) 30 | 3. Statistical decision theory, game theory, and expected utility. 31 | Admissibility, unbiasedness, minimax risk, asymptotic properties of 32 | estimators [Notes](eco517_l3.pdf) 33 | 4. Sufficient statistics, factorization theorem, Rao-Blackwell theorem 34 | [Notes](eco517_l4.pdf) 35 | 5. Maximum likelihood, Fisher information, the information (Cramér-Rao) bound, 36 | and the method of moments. [Notes](eco517_l5.pdf) 37 | 6. Large-sample properties of maximum likelihood estimators. Maximum likelihood 38 | is asymptotically normal. It is also asymptotically minimax. 39 | [Notes](eco517_l6.pdf) 40 | 7. Bayesian concepts. Complete class theorem. A good frequentist agrees with at 41 | least one Bayesian in finite samples; no frequentist is allowed to make fun 42 | of any non-dogmatic Bayesian. [Notes](eco517_l7.pdf) 43 | 8. Testing concepts. [Notes](eco517_l8.pdf) 44 | 9. Testing in small samples. Neyman-Pearson lemma. Unbiased tests. 45 | [Notes](eco517_l9.pdf) 46 | 10. Testing in large samples. Wald, Score and likelihood ratio tests are 47 | asymptotically chi-squared. They are also asymptotically optimal. [Notes](eco517_l10.pdf) 48 | 11. Confidence sets. Bernstein-von Mises theorem. All good frequentists and 49 | non-dogmatic Bayesians agree in large samples. [Notes](eco517_l11.pdf) 50 | 51 | # Errors 52 | 53 | Please report typos and errors in the lecture notes by opening an issue. 54 | --------------------------------------------------------------------------------