Instructor: Suhasini Subba Rao (office: Blocker 432).
- Time Series (Beware I have not corrected
- I am having a lot of problems including the citations in the individual
chapters (below). So please use these as an aid. The most up-to-date files are
in the notes above.
- Introduction (Chapter 1)
Homework 1: 1.1-1.6 (Due Friday 9th Sept).
- Linear Time series (Chapter 2)
Homework 2: 1.7-1.9, 2.1 and 2.4 (Due Friday 16th Sept).
This piece of code generates an
uncorrelated linear time series.
This piece of code generates causal and
noncausal AR(1) and AR(2) time series.
This piece of code generates causal
AR(2) time series, where the roots of the corresponding characteristic
polynomial are complex.
- The Autocovariance of a Linear Time series (Chapter 3)
Homework 3: The remaining HW questions in Chapter 2 (Due Monday
26th Sept). This is the code for some of the simulations code for this HW.
Please note that due to the Big Data workshop the class on (Friday)
23rd September is cancelled.
Homework 4: All problems in Chapter 3 (3.1-3.7). Due October 7th.
For chapter 4 we will consider nonlinear time series models. Here
is the code for various models
Nonlinear Time Series (Chapter 4)
(these may be edited)
Prediction (Chapter 5)
Homework 5: All problems in Chapter 4. Due October 24th.
The sample mean and covariance (Chapter 6)
(these may be edited).
The paper by Li (1992)
Homework 6: All problems in Chapter 5. Due October 31st (Exercise
5.4 is optional but fun).
Homework 7: All problems in Chapter 6. Due November 11th.
Parameter estimation (Chapter 7)
Here is a chapter on conditional expectations (taken from
Stochastic Limit Theorem, by James Davidson). Please look at Theorem
10.26 (on conditional expectations for nested sigma-algebras).
Spectral Analysis (Chapter 8)