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Instructor
Alan Yang, Instructor - Econometric Analysis: Methods and Applications

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.

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
16:46

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)
11:14

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)
10:55

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: Classical Assumptions: Detection and Correction

Lecture 15 Lesson 5: Multicollinearity (Part 1)
14:15

Understand the meaning and practical consequences of multicollinearity.

Lecture 16 Lesson 5: Multicollinearity (Part 2)
18:10

Understand how to detect severe multicollinearity in multiple regression models and possible prescription strategies in the presence of severe multicollinearity.

Quiz 5 Lesson 5 Exercise
Lecture 17 Lesson 6: Heteroskedasticity (Part 1)
16:37

Understand the meaning and practical consequences of heteroskedasticity.

Lecture 18 Lesson 6: Heteroskedasticity (Part 2)
19:31

Understand how to detect heteroskedasticity in multiple regression models using graphical methods and formal tests.

Lecture 19 Lesson 6: Heteroskedasticity (Part 3)
08:18

Understand and be able to estimate and interpret robust standard errors as a correction strategy in the presence of heteroskedasticity.

Quiz 6 Lesson 6 Exercise
Lecture 20 Lesson 7: Serial Correlation (Part 1)
18:38

Understand the meaning and practical consequences of first-order serial correlation in time-series data.

Lecture 21 Lesson 7: Serial Correlation (Part 2)
15:47

Understand how to detect first-order serial correlation using a formal test.

Lecture 22 Lecture 7: Serial Correlation (Part 3)
16:28

Understand and be able to estimate and interpret estimated GLS as a correction strategy in the presence of first-order serial correlation.

Lecture 23 Lesson 7: Serial Correlation (Part 4)
05:30

Understand and be able to estimate and interpret robust standard errors as a correction strategy in the presence of first-order serial correlation.

Quiz 7 Lesson 7 Exercise

Module 4: Practical Applications I: Binary Choice Models

Lecture 24 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 25 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 26 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 27 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 8 Lesson 8 Exercises

Module 5: Practical Applications II: Analyzing Pooled Cross-Sectional and Panel Data

Lecture 28 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 29 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 9 Lesson 9 Exercise
Lecture 30 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 31 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 32 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 33 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 10 Lesson 10 Exercise

Reviews

10 Reviews

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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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DICKSON L

May, 2017

A fantastic and interesting course. It greatly helped me learn to work on machine learning techniques to Chinese. The instructor patiently explained many important natural language processing techniques in understandable manner, though I was not familiar with Chinese language.

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Nazanin H

May, 2017

As the instructor happened to be a professional teacher, it helped me understand the quantitative and Econometric Analysis without difficulty. Since I was in the finance field, it excellently helped me to improve my performance

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Colin S

May, 2017

This course immensely helped me as a business owner to understand applications of econometric analysis.

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Zeydy O

July, 2017

The course encouraged me to learn how to predict credit importance on an operative principle. I could tell the teacher was an expert instructor, because I learned quantitative and Econometric Analysis without much difficulty.

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Michael S

July, 2017

A masterful course in Economic Analysis. I ‘ve been trying to find a course like this for a long time. Specifically, this course helped me individually polish my skillsets. Since I’m in the money management profession, it helped me become comfortable with most forms of econometric analysis at work.

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Christopher K

July, 2017

The instructor handles the subject matter in a way that is comfortable for anybody to understand. He shares competent knowledge that helped me grasp this course quickly.

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John C

July, 2017

This course definitely helped me as a commercial enterprise proprietor to learn functions of econometric analysis. You will be impressed by this course, but there is a significant degree of academic work you need to put in. Absolutely remarkable course. It helped me learn and apply economic analysis.

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