State‐Space Models and Kalman Filter
Ruey S. Tsay · Wiley series in probability and statistics · 2010
The state-space model provides a flexible approach to time series analysis, especially for simplifying maximum-likelihood estimation and handling missing values. This chapter discusses the relationship between the state-space model and the ARIMA model, the Kalman filter algorithm, various smoothing methods, and some applications. It begins with a simple model that shows the basic ideas of the state-space approach to time series analysis before introducing the general state-space model. For demonstrations, the chapter uses the model to analyze realized volatility series of asset returns, the time-varying coefficient market models, and the quarterly earnings per share of a company. Finally, it considers some applications of the state-space model in finance and business to highlight the applicability of the model and to demonstrate the practical implementation of the analysis in S-Plus with SsfPack. Controlled Vocabulary Terms ARIMA model; Kalman filter; Smoothing; time series analysis