ABSTRACT

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very im

chapter 1|16 pages

Introduction and Preparatory Analysis

chapter 2|14 pages

The Covariance Function

chapter 3|18 pages

The Power Spectrum and the Periodogram

chapter 4|22 pages

Statistical Modeling

chapter 5|12 pages

The Least Squares Method

chapter 6|20 pages

Analysis of Time Series Using ARMAModels

chapter 7|20 pages

Estimation of an AR Model

chapter 8|12 pages

The Locally Stationary AR Model

chapter 10|8 pages

Estimation of the ARMAModel

chapter 11|14 pages

Estimation of Trends

chapter 12|16 pages

The Seasonal Adjustment Model

chapter 13|14 pages

Time-Varying Coefficient AR Model

chapter 14|18 pages

Non-Gaussian State-Space Model

chapter 15|16 pages

The Sequential Monte Carlo Filter

chapter 16|12 pages

Simulation