MA in Development Studies

This Subject Includes

• Course No: HS 630
• Course: MA in Development Studies
• Semester: III
• Title: Causal Inference in Development Economics
• Stream: Economics

• Preamble / Objectives (Optional):  Introduce the students with some basic cross sectional regression techniques. Basics of causal inference from experiment and observational data will be introduced.  The use of regression to make causal inferences from observational data will be taught. This course will enable the students to apply regression while doing data analysis and do project  evaluation. The techniques will be covered taking examples from development economics.

  Course Content/ Syllabus

  Simple linear regression with two variables: Estimation, Inference; Multi variable regression: Estimation, Inference; Dummy variables in regression: Variables with multiple categories;  Heteroskedasticity: Robust regression;  Binary response Models: Logit, Probit ;   Random Assignment: Potential outcome function; Instrumental Variables Regression: Two stage regression;  Regression discontinuity; Difference-in-difference method.

   

  Books (In case UG compulsory courses, please give it as “Text books” and “Reference books”.  Otherwise give it as “References”.
  Texts: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.)
  1.	Joshua  D. Angrist and Jörn-Steffen Pischke, Mastering Metrics The Path from Cause to Effect, Princeton University Press, 2015.
  2.	Jeffrey M. Wooldridge,     Introductory Econometrics A Modern Approach, Fifth Edition, Cengage Learning, 2013.
  3.
  References: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.)
  1.	Jeffrey M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, Second Edition, MIT Press, 2002
  2.

   

  Detailed Course Content (Optional) It will not be included in the Courses of Study Booklet
  Sl. No.	Broad Title / Topics	Number of Lectures
  1	Simple two variable regression	4
  2	Multi variable regression	8
  3	Dummy variable	2
  4	Hetroskedasticity	1
  5	Binary response model	8
  6	Potential outcome function	1
  7	Instrumental variable	4
  8	Regression discontinuity design	6
  9	Difference in difference method	6
  Total Number of Lectures =		40

   

  In case of revision of existing course, Please provide below the details of existing course. EXISTING COURSE

  Course Number, Title, L-T-P-C:

  Pre-Requisite (if any)

  Contents:

  References: