SA.100.401 section 20 Syllabus

Econometrics

Course Information

Syllabus Revision: 

Please note that the syllabus, and the Class Schedule specifically, may change before or during the course. The most current syllabus is located in Canvas.

Course Information: 

Econometrics
SA.100.401.20 ( 4.0 Credits )
Spring 2024 [SB Spring 24]
Description
Provides comprehensive introduction to econometrics. Develops tools for estimating functional relationships and critically reading empirical studies that use different econometric techniques; presents assumptions of multivariate regression and discusses the most common econometric problems and the potential consequences and remedies; and discusses omitted variables, sample selection, heteroscedasticity, autocorrelation, multicollinearity and use of discrete variables. Introduces instrumental variable technique. Uses statistical software in applied exercises. Prerequisite: Statistical Methods for Business and Economics.
Department: SA Research Methods
College: Nitze School of Advanced International Studies

Course Introduction: 

This course introduces students to multiple regression and other methods used to analyze data in economics, finance, marketing and political science. Topics include: multiple linear regression, regression with discrete random variables, instrumental variables regression, analysis of random experiments and quasi-experiments, and regression with panel data. Students will learn how to conduct and critique empirical studies. The course emphasizes empirical applications; the mathematical theory of econometrics will be introduced only as needed and will not be a central focus. In the lectures, many empirical examples using a wide variety of data sets will be illustrated.

Instructor Information: 

Instructor

Additional Instructor Information and Office Hours: 

Instructor: Sergio Pastorello

Email: sergio.pastorello@gmail.com

Telephone: +39 051 20 9 2751

Office Hours: Thursday 11-12.30 am

Office: Room 211

Website: https://www.unibo.it/sitoweb/sergio.pastorello/en

Course Schedule: 

Spring 2024 [Spring 2024]
Term Start Date: Wednesday, 3-Jan-2024  Term End Date: Saturday, 15-Jun-2024
Location and Schedule:  
Schedule Detail: [01-29-2024 to 05-04-2024, Th 08:30 AM - 11:00 AM; SAIS Bologna , Belmeloro]
CRN: SA.100.401.20.SB Spring 24

Teaching Assistant Information

Teaching Assistant(s): 

Teaching Assistant: Luigi Valeri

Email: lvaleri@jhu.edu

Course Learning Objectives

Course Learning Objectives (CLOs): 

  • • Students understand and apply basic econometric techniques (mostly: OLS regression).

  • • Students understand the problem of causal inference in economics.

  • • Students understand and can criticize applications of econometric techniques.

  • • Students are able to conduct basic econometric analysis with their own data.

  • • Students are familiarized with statistical analysis using the software package Stata.

Required Text and Other Materials

Books: 

Textbooks & Materials

The textbook is J.H. Stock and M.W. Watson, Introduction to Econometrics (4th edition), Pearson, 2019. Earlier editions and all international editions printed in English are acceptable, but you will need access to the 4th edition for problem sets. Required readings will be from this book (denoted with “SW” in the Class Schedule section below).

J.M. Wooldridge, Introductory Econometrics (7th edition), Cengage, 2019 is a valid alternative covering the same material with a somewhat more mathematical treatment.

Other Materials: 

Required Software

The required software is STATA, which is available from JHU. A STATA class will be offered in parallel with the Econometrics lectures during the first half of the semester. Attendance at this class is not obligatory but is strongly recommended, particularly for students with no experience in using statistical software.

Evaluation and Grading

Grading Breakdown: 

Problem Sets

There will be four computer-based assignments over the semester. The first one will be for practice and familiarization, and the final three provide 30% (10% each) of the course mark. Students are free to work on these either individually or in groups of two or three. Please list the name(s) of those with whom you worked on your assignment and append your STATA “log” files to your assignments. Irrespective of group size, students must prepare their own reports on the assignments. Late problem sets may be submitted after deadline, but will receive a 20% per day deduction. Submission procedures will be detailed on the first problem set.

Grading

Problem Sets: 30%; Midterm Exam: 30%; Final Exam: 40%. Numerical marks will be given for each the three course components (mid-term, assignments, and final examination). These will be summed to 100 to give the overall course mark. Both exams must be taken in-person; you will be permitted to bring in a single (two-sided) A4 sheet of notes and a calculator; no textbooks, computers, cell phones, etc.

Grading Scale: 

A =Outstanding, A- =Excellent, B+ =Very Good, B =Good, B- =Pass, C+ =Low Pass, C =Minimal Pass, D =Failure 

Course Schedule

Course Schedule Outline: 

Class Schedule (preliminary)

  1. Introduction and Review of Probability and Statistics (SW Ch. 2, 3)

  2. Linear Regression with a Single Regressor (SW Ch. 4)

  3. Interval Estimation and Hypothesis Testing in Simple Linear Regression (SW Ch. 5)
    PS 1 posted
  1. Multiple Linear Regression (SW Ch. 6)
    PS 1 due
  1. Interval Estimation and Hypothesis Testing in Multiple Linear Regression (SW Ch. 7)
    PS 2 posted
  1. Nonlinear Regression Models (SW Ch. 8)
    PS 2 due
  1. Assessing Multiple Linear Regression Models (SW Ch. 9)
    Midterm Exam (coverage through Lecture 6)
  1. Panel Data Models 1 (SW Ch. 10)

  2. Panel Data Models 2 (SW Ch. 10)
    PS 3 posted
  1. Regression with Binary Dependent Variables (SW Ch. 11)
    PS 3 due
  1. Instrumental Variables Regression 1 (SW Ch. 12)
    PS 4 posted
  1. Instrumental Variables Regression 2 (SW Ch. 12)
    PS 4 due
  1. Experiments and Quasi-Experiments (SW Ch. 13)
    Course Review

Policies

Academic Policies: 

  • Student and Academic Handbook

    Student and Academic Handbook

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