Time Series Concepts for Conditional Distributions
The paper asks the question - as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant. Copyright 2003 Blackwell Publishing Ltd.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 65 (2003)
Issue (Month): s1 (December)
|Contact details of provider:|| Postal: Manor Rd. Building, Oxford, OX1 3UQ|
Web page: http://www.blackwellpublishing.com/journal.asp?ref=0305-9049
More information through EDIRC
|Order Information:||Web: http://www.blackwellpublishing.com/subs.asp?ref=0305-9049|