star twitter facebook envelope linkedin instagram youtube alert-red alert home left-quote chevron hamburger minus plus search triangle x


Applied Econometrics I


Units: 6


Econometrics has an important place in the data sciences.  As your textbook authors say, the purpose of econometrics is to “untangle cause and effect in human affairs.”  Econometrics is essential for advancing understanding in the social sciences, conducting public policy evaluation, and assessing the impact of business practice.

Both Applied Econometrics I and Applied Econometrics II are “hands on” courses in which you will not only learn to read and interpret existing studies, but will also conduct econometric analyses of your own.  The goal is to help you take your first few steps toward becoming a “Metrics Master”! One of those steps is becoming competent and confident in the use of Stata to conduct empirical analyses.

Learning Outcomes

Applied Econometrics I is the first course in a two-course sequence designed to teach the essentials of econometric methodology. You should plan to take both courses.  


During the first course you will:


  • Learn why random assignmentis so useful for the purpose of sorting out cause and effect.
  • Develop a clear understanding of bivariateand multiple regression, and come to appreciate the value and limitations of regression methods.
  • Acquire an appreciation for the use of instrumental variablesfor the purpose of evaluating causality in complex real-world applications.


Applied Econometrics IIfollows up by pursuing those same topics in additional depth, and by treating other topics and applications.  For instance, in that course you will:


  • Learn how regression discontinuityis used to draw inferences about causal effects from rules constraining human behavior.
  • Use difference-in-differences techniquesto study causality when experiments happen naturally in society.
  • Apply event study analysis andsynthetic control methodsto tackle causal questions when there are multiple natural experiments, or small sample sizes.