Modeling and forecasting cointegrated relationships among heavy oil and product prices

By Alessandro Lanza, Matteo Manera, Massimo Giovannini. 2005

170 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/57b2f637eda932c72e379af286173f4e7266e45d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Ling Tang
2. Zishu Wang
3. Lean Yu

In Economics and Econometrics

1. Ruijin Du
2. Minggang Wang
3. H. Eugene Stanley
4. Lin Chen
5. Chao Wang
6. Ling Tang
7. Longfeng Zhao
8. Zishu Wang

Citations in the corpus

• A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015, methodology): “[11] used the ECM model to predict the WTI and Brent weekly crude oil prices with the sample period from 1994 to 2002.”
• A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “The fi rst is a structural model of the price of oil, including ECM [7], VECM [8], VAR [9] et al., depending on fundamental data such as demand and supply and is implemented through the use of a linear regression.”