📊 Applied Econometrics

Table of Contents

NOTE: This page/course is under construction!

Description

This course provides an overview of both classical and recent methods for quantitative economic analysis. Through the lens of classical regression methods, students will be able to uncover patterns and extract insights from multiple types of socioeconomic data, including cross-sectional, time series, and panel data. After developing a basic conceptual understanding of these methods, students will learn how to apply them using various software packages and programming languages, including Stata, R, Geoda, and Python. Through the lens of more recent quantitative methods, students will develop a basic understanding of spatial econometrics, spatial Markov-chain modeling, and machine learning. The course also provides supplementary online lectures and tutorials on principles of statistics, regression analysis, and statistical programming for those students who need them.

Objectives

  • Use statistical methods to uncover patterns and extract insights from multiple types of socioeconomic data.
  • Be able to use multiple software packages and programming languages to analyze cross-section, time series, and panel datasets.
  • Understand some of the latest research methods in the fields of spatial econometrics, Markov chain modeling, and machine learning.

Modules

Meet your instructor

Carlos Mendez

FAQs

Are there prerequisites?

There are no prerequisites for the first course.

How often do the courses run?

Continuously, at your own pace.

Acknowledgments

We are thankful to DataCamp for providing us full access to their entire course curriculum. We also appreciate the data and code contributions of the following scholars: Christopher Baum, Mark McGillivray, Rabiul Islam, Pietro Pizzuto, and Federico Rossi.

Last updated on Jan 7, 2022

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