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Instructor

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Alan Yang

Dr. Alan Yang is a Lecturer in the Discipline of International and Public Affairs at the School of International and Public Affairs (SIPA), Columbia University. He received his PhD in Political Science at Columbia University (2003) and has more than a dozen years of experience teaching courses in Introductory Statistics, Econometrics, and Quantitative Methods in Program Evaluation at SIPA. His research has appeared in edited volumes and journals such as Public Opinion Quarterly, Political Science Quarterly, and Social Science Quarterly. He also does statistical consulting work for non-profit, NGO, and academic organizations. He and his co-author, Rodolfo de la Garza, are currently at work on a manuscript (Americanizing Latino Politics) under contract with Routledge on Latino politics in the U.S. to be published in Fall 2017.

Econometric Analysis: Methods and Applications

Instructor: Alan Yang

Quantitative and Econometric Analysis focused on Practical Applications

  • Quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing. 
  • The Instructor, Alan Yang, is a faculty member at the Department of International and Public Affairs at Columbia University where he teaches courses in Introductory Statistics, Econometrics, and Quantitative Analysis in Program Evaluation and Causal Inference.

Course Description

This course will introduce students to an applied, intermediate level of quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing. This course will focus on applied regression analysis and is intended to give students hands-on experience with real data and real analysis. The course will help you become both a sophisticated consumer of relatively advanced statistical techniques and a better practitioner in conducting your own empirical analyses. By learning econometric methods and applications, students will develop the capacity to build the kind of predictive models that enhance decision making when faced with uncertainty in real world contexts. These tools and skills will also enable students to perform analyses that, under some circumstances, allow us to make valid causal inferences about the effect of a program or intervention on an outcome of interest. The course begins with a recap of simple and multiple linear regression, and then moves to techniques for analyzing real-world quantitative data: incorporating variables in regression analysis that are categorical as well as quantitative, and considering the interactions between independent variables. We will consider model specification in practice—how to choose our independent variables, and how to model the correct functional form. Students will learn how to model nonlinear functional relationships using OLS through transformations of the data. We consider important assumptions that must be fulfilled in order that we obtain credible estimates of our predictors of interest, how to diagnose departures from these assumptions, and practical correction strategies. We follow this with select topics of special interest including modeling binary dependent variables, and the analysis of pooled-cross sectional and panel data. Lectures will include examples in STATA format, a widely used statistical package in the social sciences and business programs. All course exercises, however, will be designed and presented in both STATA and R. Each lesson will include an instructional component and an exercise to give you an opportunity to apply the methods and techniques using actual data. Basic instruction (i.e., sample syntax) will be provided in both STATA and R for all exercises.

What am I going to get from this course?
Answer questions such as these: 
  • What is the impact of non-traditional factors in predicting credit worthiness?
  • What is the effect of a country's resource abundance in promoting economic growth? 
  • What are the key financial determinants of loan application approval, all else being equal? 
  • What is the impact of air pollution levels on median neighborhood housing prices? 
  • What is the estimated gender-wage gap, all else being equal (and how does the wage gap vary by level of education)?

Prerequisites and Target Audience

What will students need to know or do before starting this course?
  • The course requires knowledge of basic probability and statistics, but does not assume proficiency with linear algebra or calculus.  
Who should take this course? Who should not?
  • Undergraduate and graduate students in disciplines that emphasize quantitative analysis and working professionals with prior experience with basic statistics may see significant benefit from this course

Curriculum

Module 1: The Fundamentals of Applied Regression Analysis
Lecture 1 Lesson 1: Linear Regression Recap

Understand why randomized experiments are the gold standard in estimating credible estimates of a treatment effect. Understand the importance of controlling for confounding characteristics in estimating a “treatment effect” using multiple linear regression when analyzing observational data.

Quiz 1 Lesson 1 Exercise & Answer Key
Lecture 2 Lesson 2: Multiple Regression in Practice (Part 1)
19:58

Understand and be able to interpret coefficient estimates on binary and quantitative explanatory variables. Understand and be able to interpret coefficient estimates on an interaction between binary and quantitative explanatory variables.

Lecture 3 Lesson 2: Multiple Regression in Practice (Part 2)
14:51

Understand and be able to interpret coefficient estimates on categorical explanatory variables with more than two categories.

Lecture 4 Lesson 2: Multiple Regression in Practice (Part 3)
21:45

Understand and be able to interpret coefficient estimates on an interaction between two quantitative explanatory variables.

Quiz 2 Lesson 2 Exercise and Answer Key
Module 2: Model Specification in Theory and Practice
Lecture 5 Lesson 3: Model Specification I (Part 1)
12:41

Understand the concept of the population regression function and the key properties of OLS slope estimators when the Classical Assumptions are fulfilled.

Lecture 6 Lesson 3: Model Specification I (Part 2)
14:52

Understand the meaning and consequences of omitted variable bias. Be able to anticipate the nature of the bias associated with omitted variable(s) in linear regression models.

Lecture 7 Lesson 3: Model Specification I (Part 3)
27:32

Understand how careful selection of the right explanatory variables can reduce the bias associated with coefficient estimate on particular explanatory variables of interest. Understand best practices in choosing between alternative model specifications.

Lecture 8 Lesson 3: Model Specification I (Part 4)
05:21

Understand the meaning and practical consequences of including an irrelevant explanatory variable.

Quiz 3 Lesson 3 Exercise
Lecture 9 Lesson 4: Model Specification II (Part 1)
11:14

Understand when a linear functional form is appropriate in regression analysis. Be able to detect nonlinear functional forms using graphical and numerical summaries.

Lecture 10 Lesson 4: Model Specification II (Part 2)
22:04

Understand and be able to implement natural logarithmic transformations of quantitative variables. Understand and be able to interpret coefficient estimates from linear-log models, and when a linear-log model is appropriate.

Lecture 11 Lesson 4: Model Specification II (Part 3A)

Understand and be able to interpret coefficient estimates from log-linear models, and when a log-linear model is appropriate.

Lecture 12 Lesson 4: Model Specification II (Part 3B)

Case study to understand how to interpret coefficient estimates on interactions in log-linear models.

Lecture 13 Lesson 4: Model Specification II (Part 4)
23:07

Understand and be able to interpret coefficient estimates from log-log models, and when a log-log models is appropriate.

Lecture 14 Lesson 4: Model Specification II (Part 5)
12:29

Understand and be able to interpret coefficient estimates from polynomial models, and when a polynomial models is appropriate.

Quiz 4 Lesson 4 Exercises
Module 3: Practical Applications I: Binary Choice Models
Lecture 15 Lesson 8: Binary Dependent Variable Models (Part 1)
27:13

Understand and be able to estimate and interpret a linear probability model (LPM) when modeling a binary dependent variable. Understand the strengths and limitations of LPM’s.

Lecture 16 Lesson 8: Binary Dependent Variable Models (Part 2)
29:05

Understand and be able to estimate and interpret binary logistic regression model, and the advantages to the logit model over the LPM. Understand and be able to interpret logit coefficient estimates as odds ratios. Understand and be able to estimate and interpret predicted probability changes of a successful outcome (holding all else at specific values).

Lecture 17 Lesson 8: Binary Dependent Variable Models (Part 3)
15:18

Understand and be able to compare and contrast the results of LMP’s and logit models.

Lecture 18 Lesson 8: Binary Dependent Variable Models (Part 4)
23:41

Understand best practices in choosing a model specification in binary response models and how to interpret resulting output.

Quiz 5 Lesson 8 Exercises
Module 4: Practical Applications II: Analyzing Pooled Cross-Sectional and Panel Data
Lecture 19 Lesson 9: Pooled Cross-Sectional Data (Part 1)
18:07

Understand and be able to estimate and interpret coefficient estimates from models using pooled cross-sectional data with two or more time periods.

Lecture 20 Lesson 9: Pooled Cross-Sectional Data (Part 2)
36:21

Understand and be able to estimate and interpret coefficient estimates from Difference-in- Differences (DID) models that use two periods of pooled cross-sectional data. Understand the key assumptions associated with DID models, and when this analysis strategy is appropriate.

Quiz 6 Lesson 9 Exercise
Lecture 21 Lesson 10: Panel Data Analysis (Part 1)
13:35

Understand the problem of unobservable heterogeneity and the limitations of pooled OLS as a strategy for analyzing panel data.

Lecture 22 Lesson 10: Panel Data Analysis (Part 2)
13:29

Understand and be able to estimate and interpret first-difference models when you have two periods of panel data. Understand how first-differences can overcome the problems associated with using pooled OLS to analyze panel data (if certain assumptions hold).

Lecture 23 Lesson 10: Panel Data Analysis (Part 3)
19:24

Understand and be able to estimate and interpret deviation from means (fixed effects) and least squares dummy variable models when you have panel data. Understand the concept of unit and time fixed effects and how bias may be reduced when estimating fixed effects models (if certain assumptions hold).

Lecture 24 Lesson 10: Panel Data Analysis (Part 4)
11:50

Understand and be able to interpret the results from a case study on the impact of seat belt usage on traffic fatalities at the state level using a fixed effects approach.

Quiz 7 Lesson 10 Exercise

Reviews

3 Reviews

Empty user
Nick B

December, 2016

Instructor delivers sessions very precisely, and everyone can understand these topics easily.

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Tracy G

December, 2016

Everything is well articulated and to the point making it easy to grasp the concepts. Great course thus far, excellent content, clear explanations.

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Pargat S

December, 2016

I was having a hard time with my econometrics course at university and this course has helped understand the concepts better.

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