Conditional independence modeling can reveal the relationship between a set of variables, using sample partial correlations in a least squares context. The method is applied to contemporaneous and lagged variables of a multivariate time series to identify structural AR and ARIMA models. These allow simultaneous as well as lagged dependence between the series. Examples of banking and dollar term interest rates illustrate how models with a relatively sparse parameterisation can be constructed although a unique identification is not always possible. The talk will be followed by coffee and cookies in MC 6123. ALL WELCOME!
Have you ever considered what ingredients go into the creation of a well-designed course? While we do not advocate one set "recipe," a number of common elements exist for you to consider. In this workshop, we will discuss key elements involved in course design, generate and consider important issues related to each of these elements, and learn about the interrelationships amongst the elements. This workshop will be highly interactive and will use guided facilitation to create a flexible course design model. To prepare for this workshop, please consider a course you would like to teach. Your course will provide a context for you during our many brainstorming exercises. The workshop is open to all graduate students at UW, so please pass this notice on to your colleagues. Remember to bring your lunch. Juice will be provided. Please register for this workshop by sending an email to trace@watserv1 by Friday, June 11, 1999, or by sending the form below to TRACE, MC 4055. If you have any questions, please contact TRACE at ext. 3132.