Forecasting of density functions with an application to cross-sectional and intraday returns
Year of publication: |
2019
|
---|---|
Authors: | Kokoszka, Piotr ; Miao, Hong ; Petersen, Alexander ; Shang, Han Lin |
Published in: |
International journal of forecasting. - Amsterdam [u.a.] : Elsevier, ISSN 0169-2070, ZDB-ID 283943-X. - Vol. 35.2019, 4, p. 1304-1317
|
Subject: | Compositional data analysis | Constrained functional time series | Density function forecasting | Log quantile density transformation | Prognoseverfahren | Forecasting model | Statistische Verteilung | Statistical distribution | Zeitreihenanalyse | Time series analysis | Schätztheorie | Estimation theory |
Type of publication: | Article |
---|---|
Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Notes: | Erratum enthalten in: International journal of forecasting, Volume 37, issue 3 (July/September 2021), Seite 1310-1311 |
Other identifiers: | 10.1016/j.ijforecast.2019.05.007 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
-
Nonparametric forecasting of multivariate probability density functions
Guégan, Dominique, (2018)
-
Allen, David E., (2007)
-
Bekiros, Stelios, (2015)
- More ...
-
Neural network prediction of crude oil futures using B-splines
Butler, Sunil, (2021)
-
Wasserstein autoregressive models for density time series
Zhang, Chao, (2021)
-
Kokoszka, Piotr, (2017)
- More ...