Forecasting of Discrete Time Series

Yuriy Kharin
Additional contact information
Yuriy Kharin: Belarusian State University, Department of Mathematical Modeling and Data Analysis

Chapter Chapter 10 in Robustness in Statistical Forecasting, 2013, pp 305-352 from Springer

Abstract: Abstract This chapter is devoted to forecasting in the non-classical setting where the state space of the time series is finite, necessitating the use of discrete-valued time series models. The field of discrete statistics has remained relatively underdeveloped until the recent years, when rapid introduction of digital equipment stimulated the researchers to develop numerous discrete models and techniques. In this chapter, we discuss optimal forecasting statistics and forecast risks for Markov chain models, including high-order Markov chains, and the beta-binomial model.

Keywords: Markov Chain; Stationary Probability Distribution; Partial Connection; Distortion Level; Discrete Time Series (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-00840-0_10

Ordering information: This item can be ordered from
http://www.springer.com/9783319008400

DOI: 10.1007/978-3-319-00840-0_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().