Time Series Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Daniel J. Henderson10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
This seminar will introduce time series methods for univariate and multivariate models.
A time series is a collection of data points for a particular unit of observation (e.g., a firm) measured over time, typically at regular intervals (e.g., monthly). Time series analyses allow researchers to predict future behavior—for example, they are often used for tasks as diverse as predicting stock returns or weather patterns to monitoring patients in a hospital setting (e.g., heart rate monitoring).
We will focus both on developing intuition about time series methods and how to program and apply these methods in practice. We will pay particular attention to how to present results, both graphically and via computer output, in ways that differ from the cross-sectional setting that most researchers are familiar with.
Starting August 13, we are offering this seminar as a 4-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. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
We will begin with the simple case of a univariate stationary time series. We will discuss the theory of how to recognize autoregressive moving average (ARMA) processes, as well as the intuition behind estimation of these models via maximum likelihood. We will pay special attention to making sure the underlying assumptions of our ARMA models are satisfied in practice, and then use these models to generate forecasts. Once we have a solid foundation in stationary models, we will discuss how to recognize and address non-stationary processes. We will then move onto multivariate times series models and address causality.
Along the way we will discuss practical issues, including how to pick the order of lags or the polynomial order for the trend. Each day will include plenty of hands-on practice, so you will leave with both a firm grasp of the theoretical underpinnings of time series methods and a clear understanding of how to apply them to your own work.
We will begin with the simple case of a univariate stationary time series. We will discuss the theory of how to recognize autoregressive moving average (ARMA) processes, as well as the intuition behind estimation of these models via maximum likelihood. We will pay special attention to making sure the underlying assumptions of our ARMA models are satisfied in practice, and then use these models to generate forecasts. Once we have a solid foundation in stationary models, we will discuss how to recognize and address non-stationary processes. We will then move onto multivariate times series models and address causality.
Along the way we will discuss practical issues, including how to pick the order of lags or the polynomial order for the trend. Each day will include plenty of hands-on practice, so you will leave with both a firm grasp of the theoretical underpinnings of time series 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 who want to apply time series methods to their data in an academic setting or at their workplace. It is strongly recommended that participants be familiar with linear regression. No prior experience with linear algebra is necessary.
This course will be useful for those who want to apply time series methods to their data in an academic setting or at their workplace. It is strongly recommended that participants be familiar with linear regression. No prior experience with linear algebra is necessary.
Seminar outline
Day 1
-
- Mathematical tools for time series analysis
- Difference equations
- Lag operators
- Higher order difference equations
- Modeling and estimation for univariate stationary time series models
- Intuition
- Autoregressive moving average processes
- Estimation
- Coding
- Times series plots
- Correlograms
- Ordinary least-squares
- Maximum likelihood estimation
Day 2
-
- Diagnostic checking
- Analysis of residuals
- Formal statistical tests
- Forecasting
- In-sample forecasts
- Out-of-sample forecasts
- Point, interval, and density forecasts
- Coding
- Diagnostic tests
- In-sample forecasts
- Out-of-sample forecasts
- Point, interval, and density forecasts
Day 3
-
- Multivariate stationary time series models
- Cross-correlation function
- Estimation
- Nonstationary times series models
- Deterministic trends
- Stochastic trends
- Unit root testing
- Coding
- Multivariate models
- Deterministic trends
- Stochastic Trends
- Unit root tests
Day 4
-
- Structural breaks
- Theory
- Estimation
- Interrupted time series
- Multivariate nonstationary time series models
- Spurious regression
- Vector Autoregression
- Coding
- Interrupted time series
- Vector autoregression
Day 1
-
- Mathematical tools for time series analysis
- Difference equations
- Lag operators
- Higher order difference equations
- Modeling and estimation for univariate stationary time series models
- Intuition
- Autoregressive moving average processes
- Estimation
- Coding
- Times series plots
- Correlograms
- Ordinary least-squares
- Maximum likelihood estimation
- Mathematical tools for time series analysis
Day 2
-
- Diagnostic checking
- Analysis of residuals
- Formal statistical tests
- Forecasting
- In-sample forecasts
- Out-of-sample forecasts
- Point, interval, and density forecasts
- Coding
- Diagnostic tests
- In-sample forecasts
- Out-of-sample forecasts
- Point, interval, and density forecasts
- Diagnostic checking
Day 3
-
- Multivariate stationary time series models
- Cross-correlation function
- Estimation
- Nonstationary times series models
- Deterministic trends
- Stochastic trends
- Unit root testing
- Coding
- Multivariate models
- Deterministic trends
- Stochastic Trends
- Unit root tests
- Multivariate stationary time series models
Day 4
-
- Structural breaks
- Theory
- Estimation
- Interrupted time series
- Multivariate nonstationary time series models
- Spurious regression
- Vector Autoregression
- Coding
- Interrupted time series
- Vector autoregression
- Structural breaks
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.