STAT 463: Applied Time Series Analysis
MWF 9:05am – 9:55am, 215 Thomas
Instructor: Scott Roths,
Office/hours: 416 Thomas, Tue 10am – 12pm
Voicemail: 865-3131
TA: Ben Straub,
TA office/hours: 424 Thomas, Mon 10am – 12pm
Description: Time series is concerned with identification of models and forecasting for empirical data collected over time. We will also be working with R software. This can be downloaded free for MAC or PC here http://www.r-project.org/
Text: The text for this course is Time Series Analysis, 2e, by Cryer and Chan. The ISBN is 978-0-387-75958-6. An electronic version of this is also available from our PSU library.
Course Website: All assignments, announcements, and due dates are posted on the PSU Canvas page for this course. Please check it daily.
Grading: Required work is divided into the following categories:
25% Homework assignments
45% Midterm exams
30% Comprehensive final exam
Homework: The homework consists of weekly exercises assigned throughout the week and due the following Wednesday during class. There will be 11 assignments total, but the lowest score will be dropped. Late work is not accepted. Collaborative work is allowed, but each of you must write up and submit your own answers; copying is not allowed.
Exams: The three in-class midterms are scheduled for September 15, October 13, and November 10 (all Fridays). The final exam will be cumulative; the date will be available later. All exams are closed-book, but you may bring a calculator and a single, two-sided sheet of notes (2 such sheets allowed for the final). Conflicts on exam dates must be resolved in advance. Allow 24 hours for email response.
Letter Grades: Semester grades are assigned according to this scale. Rounding is to the nearest whole percentage point.
93 – 99% A 77 – 79% C+
90 – 92% A- 70 – 76% C
87 – 89% B+ 60 – 69% D
83 – 86% B 0 – 59% F
80 – 82% B-
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Tentative List of Topics:
Weeks 1 - 4
· Review of statistical properties and expectations
· Statistical software: R
· Stationarity
· Regression methods and trends
· ARMA models and their properties
Weeks 5 – 8
· Differencing
· ACF/PACF
· Estimation and bootstrapping
· ARIMA models
· Model diagnostics and residual analysis
Weeks 9 – 12
· Forecasting and prediction
· Seasonal models
· Lagged regression models
· Regression with correlated errors
Weeks 13 – 16
· ARCH/GARCH models
· Smoothing techniques
· Spectral analysis
· Spectral density estimation
· Bootstrap estimation
STAT 463 Learning Objectives
Upon successful completion of this course, students are expected to understand
1) the important graphical features of a time series, including seasonal and non-seasonal trends
2) the distinction between independent and correlated data and how correlated errors complicate the assumptions of the usual regression model
3) the properties of stationarity, including the definitions of autocovariance and autocorrelation
4) how to difference time series data to remove seasonal and non-seasonal trends
5) the properties of ARMA models, including the distinction between autoregressive and moving average components
6) how to estimate the parameters for ARMA models and how to choose among several candidate models based on diagnostic techniques
7) how to use estimated time series models to predict unknown observations ahead in time
8) how to use R software to perform EDA, model fitting, model diagnostics, forecasting and simulation for time series
9) how to use and interpret R software output from time series modeling exercises, in combination with theoretical understanding of time series modeling, to gain practical insights into the structure of real-world time series