|
|
Sofia Berto Villas-Boas |
|
|
Class of 1934 Robert Gordon Sproul Distinguished Professor in Agricultural Economics |
||
|
Email: sberto@berkeley.edu U California, Berkeley - Department of Agricultural & Resource Economics, 223 University Hall, CA 94720-3310 |
SIEPR- Giannini Data Center CURRENT TEACHING OLD TEACHING NOTES AND MATERIALS
This course is an introduction to applied econometrics. Econometrics is the application of statistical techniques to the analysis of economic questions. The goals for this course is that you all:
- Learn the basic of econometrics through real policy analysis and economic research questions, so that you learn to use econometrics for answering economic questions.
- Be exposed even superficially to the analysis of binary data, time series, and panel data, and to program evaluation, with real examples, so that you will know to recognize what needs to be done when faced with this sort of data or situation.
- Learn to conduct analysis with a sophisticated software (STATA and or R), a highly valuable skill on the job market.
- Learn to be critical of regression results interpreted as causal, and how to build an argument towards causal inference.
COURSE CAPTURES: I will always record all lectures and I post remote capture (most likely in the room the technology is of slides and my voice) after every lecture on bcourses, and plan to do so in the future
Prerequisites for EEP 118:
Statistics 2 or equivalent. The material will be presented with minimum mathematics. A mathematics, or linear algebra course; and a Statistics, or Data Analysis course are required for successful completion of this course. In particular, it is assumed that all students have knowledge of calculus and linear algebra, and are familiar with basic multivariate calculus (first and second derivatives and how to obtain them), summation and integration, matrices and matrix operations. More generally, students are expected to be comfortable following mathematical arguments in the lectures and the textbook. In addition, it is assumed that all students have knowledge of topics covered in the Data Analysis or a Statistics course. More specifically, students need to know: the difference between the population and the sample; the difference between a parameter and an estimator; the properties of random variables (including both discrete and continuous random variables); how to calculate expectations, variances, correlations and conditional expectations; and the construction of confidence intervals and hypothesis tests. Some of these topics will be briefly reviewed but at a fast pace, designed for people who have already learned this material in a previous course.
This is the second course in the graduate Econometrics sequence, immediately following ARE 211, which
covered probability theory. You are expected to have a working knowledge of linear algebra. The goal of
this course is to provide you with an in-depth understanding the classical multiple linear regression model
and what happens when we relax the assumptions of this model. Lectures will focus on technical
material and increasingly provide illustrations of applications. Assignments will deal with the application of
techniques covered in class to (real world) data sets as well as the voluntary completion of several proofs.
Two in class (with a take home component) exams will test your understanding of the technical material.
There will be two lectures each week. The discussion section will focus on computation.
COURSE CAPTURES: I will always record all lectures and I post remote capture (most likely in the room the technology is of slides and my voice) after every lecture on bcourses, and plan to do so in the future
Go BEARS! and Go Bears - Yosemite, 2008 May .
Copyright 2019-2021
