exercises for Thu Feb 10

1. Note that the Nils Collection of Exercises and Lecture Notes are placed here at the course website. The first version has 6 pages, and there will soon be more.

2. On Thu 3 we did the exercises listed, from Ch 1, and also discussed in some gerenality the theory and practical algorithmic application of the Grand Theorem for parametric models: the ML estimator is approximately normal and unbiased, with covariance matrix approximately equal to the inverse observed Fisher information matrix. See the com1d script for an illustration, with the JJ data.

3. We rounded off Ch 1 and started Ch 2.

4. Exercises for Thu Feb 10: First, do the JJ dataset, playing with one or two more models, using perhaps the com1d as the starting point. Then, do Nils Exercises 5, 6. Then, model and analyse the *chicken* dataset of the book (available in the astsa package), using "linear regression plus correlated error terms", and attempt the differencing operator. For each attempted model, check estimated residuals.