I expect that students read the suggested literature specific to linear econometrics, including the basic texts on mathematical econometrics, probability, and statistical inference, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate cross-section data in R and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies.
Teaching Assistants:. Tutoring hours: Thursday, am to pm to be updated. Download the complete syllabus here. Data Set Handbook. Datasets for Assignment 1. Class 1 — Simulation Probability Distributions in R.
Class 3 — How to reproduce examples throughout the chapter Chapter 2 — Wooldridge. Class 4 — How to reproduce examples throughout the chapter Chapter 3 — Wooldridge. Class 5 — How to reproduce examples throughout the chapter Chapter 4 — Wooldridge. Class 6 — How to reproduce examples throughout the chapter Chapter 6 — Wooldridge.
Class 7 — How to reproduce examples throughout the chapter Chapter 7 — Wooldridge. Class 8 — How to reproduce examples throughout the chapter Chapter 8 — Wooldridge. Class 5 — Asymptotic Theory. Chapter 1 — The nature of econometrics and economic data. Chapter 2 — The simple regression model.
Chapter 3 — Multiple Regression Analysis: Estimation. Chapter 4 — Multiple Regression Analysis: Inference. Chapter 8 — Heteroskedasticity. Chapter 1 — Problems and Computer Exercises. Chapter 2 — Problems and Computer Exercises. Chapter 3 — Problems and Computer Exercises. Chapter 4 — Problems and Computer Exercises. Chapter 6 — Problems and Computer Exercises. Chapter 7 — Problems and Computer Exercises.
Chapter 8 — Problems and Computer Exercises. Chapter 9 — Problems and Computer Exercises. Chapter 15 — Problems and Computer Exercises. Introduction to R. Basic R Manipulation objects and basic functions. PNAD in R. Introduction to R and R Studio. How to accommodate nonlinearities in LRM variables transformation. R Markdown Cheat Sheet.
Basic Mathematical Tools. Fundamentals of Probability. Fundamentals of Mathematical Statistics. Unlike other books on similar topics, it does not attempt to provide a self-contained discussion of econometric models and methods.
Instead, it builds on the excellent and popular textbook " Introductory Econometrics " by Jeffrey M. Some other editions and versions work as well, see below.
It is compatible in terms of topics, organization, terminology and notation, and is designed for a seamless transition from theory to practice. Topics include:. A gentle introduction to R Data wrangling and graphics with the tidyverse Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports and research papers combining R with R Markdown or LaTeX.
Assuming the reader is familiar with the concepts discussed there, this book explains and demonstrates how to implement everything in R and replicates many textbook examples.
We also open some black boxes of the built-in functions for estimation and inference by directly applying the formulas known from the textbook to reproduce the results. Some supplementary analyses such as Monte Carlo simulations provide additional intuition and insights.
It can also be useful for readers who are familiar with econometrics and possibly other software packages, such as Stata.
For them, it offers an introduction to R and can be used to look up the implementation of standard econometric methods. All computer code used in this book can be downloaded to make it easier to replicate the results and tinker with the specifications.
The new Section 1. This set of packages offers a convenient, powerful, and recently very popular approach to data manipulation and visualization. Knowledge of the tidyverse is not required for the remainder of the book but very useful for working with real world data.
Section 1. It now stresses the use of the packages haven and rio which are newer and for most applications both more powerful and more convenient than the approaches presented in the first edition. There is a new R package "wooldridge" by Justin M. Shea and Kennth H. It very conveniently provides all example data sets.
0コメント