Nonparametric and Semiparametric Statistics - Online Course
A 3-Day Livestream Seminar Taught by
Daniel J. Henderson10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar will introduce nonparametric and semiparametric regression for cross-sectional and longitudinal (panel) data models.
Nonparametric and semiparametric methods are useful whenever we aren’t certain what the correct functional form is for the relationship between two variables—which in many fields is most of the time. Misspecifying the functional form can lead to inconsistent estimates as well as incorrect policy prescriptions. For example, estimating a curvilinear relationship as linear would give the mistaken impression that an effect is constant, when in reality the magnitude (and possibly the direction) of the effect differs across units. Using nonparametric and semiparametric methods can help detect and correct such problems.
We will focus both on developing intuitions about nonparametric regression and on how to program and apply these methods in practice. We will pay particular attention to how to present the results from nonparametric regressions, as this is quite different from the standard linear regression models that many researchers are familiar with.
Starting January 18, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions.
More details about the course content
We will begin with the simple case of nonparametric regression with a single regressor, which will allow us to probe issues of bias, variance, and inference, with an eye toward understanding local-estimation which is foundational to more advanced nonparametric methods. We will then move on to multivariate models. Here we will discuss the curse of dimensionality (the primary criticism of nonparametric methods), how it arises, why we should be mindful of it, and how we can avoid it. We will also discuss mixed data types (both continuous and discrete regressors). Finally, we will introduce advanced methods, including two of the most popular forms of semiparametric regression—partially linear models and varying coefficient models, as well as nonparametric methods for longitudinal (panel) data.
Along the way we will discuss practical issues, including choosing the appropriate bandwidth for your analysis and testing model assumptions. Each day will include plenty of hands-on practice, so you will leave with both a firm grasp of the theoretical underpinnings of nonparametric and semiparametric methods and a clear understanding of how to apply them to your own work.
We will begin with the simple case of nonparametric regression with a single regressor, which will allow us to probe issues of bias, variance, and inference, with an eye toward understanding local-estimation which is foundational to more advanced nonparametric methods. We will then move on to multivariate models. Here we will discuss the curse of dimensionality (the primary criticism of nonparametric methods), how it arises, why we should be mindful of it, and how we can avoid it. We will also discuss mixed data types (both continuous and discrete regressors). Finally, we will introduce advanced methods, including two of the most popular forms of semiparametric regression—partially linear models and varying coefficient models, as well as nonparametric methods for longitudinal (panel) data.
Along the way we will discuss practical issues, including choosing the appropriate bandwidth for your analysis and testing model assumptions. Each day will include plenty of hands-on practice, so you will leave with both a firm grasp of the theoretical underpinnings of nonparametric and semiparametric methods and a clear understanding of how to apply them to your own work.
Computing
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This course will be useful for those wanting to apply nonparametric and semiparametric techniques to their data in academic research. The course will cover basic to advanced topics but will do so via the lens of (the well-known technique of) weighted least-squares. It is expected that participants will have had a course in linear regression and preferably some experience with longitudinal/panel data methods. No prior experience with linear algebra is necessary.
Optional textbook:
Applied Nonparametric Econometrics, Henderson and Parmeter, Cambridge University Press, 2015
This course will be useful for those wanting to apply nonparametric and semiparametric techniques to their data in academic research. The course will cover basic to advanced topics but will do so via the lens of (the well-known technique of) weighted least-squares. It is expected that participants will have had a course in linear regression and preferably some experience with longitudinal/panel data methods. No prior experience with linear algebra is necessary.
Optional textbook:
Applied Nonparametric Econometrics, Henderson and Parmeter, Cambridge University Press, 2015
Seminar outline
Day 1
-
- Nonparametric regression with a single regressors
- Intuition
- Theory
- Estimation
- Standard errors
- Bandwidth selection
- Presenting results
- Coding
Day 2
-
- Nonparametric regression with multiple regressors
- Curse of dimensionality
- Product kernels
- Mixed discrete and continuous regressors
- Estimation
- Binary outcomes
- Standard errors
- Bandwidth selection
- Presenting results
- Coding
- Testing
- Confidence intervals
- Correct parametric specification
- Irrelevant regressors
- Heteroskedasticity
- Bootstrapping
- Coding
Day 3
-
- Semiparametric regression
-
- Partially linear models
- Varying coefficient models
- Estimation
- Standard errors
- Presenting results
- Coding
- Longitudinal (panel) data models
- Random effects estimation
- Fixed effects estimation
- Conditional expectation
- Gradient
- Discrete regressors
- Hypothesis testing
- Poolability
- Correct parametric specification
- Hausman tests
Day 1
-
- Nonparametric regression with a single regressors
- Intuition
- Theory
- Estimation
- Standard errors
- Bandwidth selection
- Presenting results
- Coding
- Nonparametric regression with a single regressors
Day 2
-
- Nonparametric regression with multiple regressors
- Curse of dimensionality
- Product kernels
- Mixed discrete and continuous regressors
- Estimation
- Binary outcomes
- Standard errors
- Bandwidth selection
- Presenting results
- Coding
- Testing
- Confidence intervals
- Correct parametric specification
- Irrelevant regressors
- Heteroskedasticity
- Bootstrapping
- Coding
- Nonparametric regression with multiple regressors
Day 3
-
- Semiparametric regression
-
- Partially linear models
- Varying coefficient models
- Estimation
- Standard errors
- Presenting results
- Coding
- Longitudinal (panel) data models
- Random effects estimation
- Fixed effects estimation
- Conditional expectation
- Gradient
- Discrete regressors
- Hypothesis testing
- Poolability
- Correct parametric specification
- Hausman tests
-
- Semiparametric regression
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.