Statistics 520, Spring 2007
Class meetings are TTh, 3.00-4.15pm, REC 307
Time Series and Applications
Professor M. Levine
Office hours: TTh, 2.00-3.00pm, HAAS 154
Introduction: Chapter 1
Homework 1: Pr. 1.4, 1.5, 1.7, 1.10, 1.18 Due on Tue, Jan 30th
In the Pr. 18, run several tests for dependence and report your results at some significance level.
Also, when doing the forecasting, try to forecast both 24 and 48 months ahead; try to compare precision of these forecasts by visual comparison
Solution to the HW #1 us here
Homework 2: Pr. 2.1, 2.2, 2.4, 2.8 and additional problems. Due on Tue, Feb 6th The solution is here
Some of the data sets we use in the first two-chapters:
Monthly simple returns of IBM stock from Jan 1926 to Dec 1997
Quarterly GNP (Gross National Product) of the US from 1947 to 1991
Monthly US Unemployment Rate Data
Code for simple prediction using AR(3) mode fitted for the CRSP value-weighted index data
Simple autoregressive (AR) models: properties, basics of model selection and parameter estimation
Homework 3 is here. It is due on Thursday, Feb 15th. The solution is here
Moving average (MA); the general ARMA/ARIMA models
Homework 4: Pr. 3.1, 3.3, 5.1, 5.3 and an additional problem. Due on Tue, March 7th. The Solution is here
Time series estimation methods
Time series forecasting. Here is a data example (code) and a dataset
Homework 5 is here (two problems) plus problem 5.9 from the textbook. It is due next Tue, March 27th
Nonstationarity in time series
Heteroskedasticity in time series and volatility models. Here is an example of GARCH analysis code in R
Homework 6 is here. It is due on Tue, Apr 10th
Frequency domain theory: spectral density and its properties
Homework 7: Pr. 4.1, 4.2, 4.4, 4.7 plus two additional questions. In the problem 4.7, feel free to use R instead of ITSM or any other statistical software you like.
is here. It is due on Thr, Apr 19th
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