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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Daniel J. Henderson, Ph.D

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.

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) models, 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.