ABSTRACT

Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields

chapter 1|52 pages

Stationary Time Series

chapter 2|20 pages

Linear Filters

chapter 3|86 pages

ARMA Time Series Models

chapter 4|20 pages

Other Stationary Time Series Models

chapter 5|18 pages

Nonstationary Time Series Models

chapter 6|38 pages

Forecasting

chapter 7|44 pages

Parameter Estimation

chapter 8|48 pages

Model Identification

chapter 9|24 pages

Model Building

chapter 10|50 pages

Vector-Valued (Multivariate) Time Series

chapter 11|34 pages

Long-Memory Processes

chapter 12|44 pages

Wavelets

chapter 13|40 pages

G-Stationary Processes