Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

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 Remote Seminar Taught by Jon Pevehouse, Ph.D.

Time series data are incredibly common in the natural and social sciences.  Any process that is measured repeatedly over time yields data with properties that must be properly modeled.

This seminar covers statistical methods used to analyze these data-generating processes that occur over time. Many textbooks and courses on times series focus on techniques that merely adjust for temporal variation and dependencies. In contrast, this seminar treats time series properties as phenomena of substantive interest, not simply as a statistical nuisance.

The course introduces participants to time series methods in the context of applications to various types of data. It begins with a discussion of univariate models and diagnostic tests, including autoregressive moving average (ARMA) models, interrupted time series analysis, autoregressive conditional heteroskedastic (ARCH) models, and tests for stationarity including fractional non-stationarity.

The course then moves to multivariate models including time series regression, reduced form methods (Granger causality and vector autoregression), cointegration, and error correction models. The aim is to provide a working knowledge of important time series diagnostic tests and models.

The seminar will include lab sessions to provide practice implementing the methods, using data provided by the instructor.