Time Series Analysis

Jonathon D. Brown
Additional contact information
Jonathon D. Brown: University of Washington, Department of Psychology

Chapter Chapter 13 in Advanced Statistics for the Behavioral Sciences, 2018, pp 449-494 from Springer

Abstract: Abstract Many phenomena unfold over time. For example, stock prices rise and fall, diseases run their course, and relationships ebb and flow. Occurrences like these create a time series — a sequence of observations identified by the order in which they occur. Owing to properties of inertia and persistence, the observations in a time series tend to change slowly and are frequently characterized by dependencies. Consequently, previous observations provide information about present observations, and ordinary least squares is an inefficient way to estimate the data.

Date: 2018
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:978-3-319-93549-2_13

Ordering information: This item can be ordered from
http://www.springer.com/9783319935492

DOI: 10.1007/978-3-319-93549-2_13

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 ().