Identification of vector AR models with recursive structural errors using conditional independence graphs

By Marco Reale, Granville Tunnicliffe Wilson. 2001

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c0942adacf48a94c197bfe31099859bf28364a1a

How researchers see this paper

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

In Artificial Intelligence

1. Michael Eichler
2. Bryon Aragam

In Control and Systems Engineering

1. Michael Eichler
2. Matteo Barigozzi
3. Marc Hallin

In Statistics and Probability

1. Michael Eichler
2. Bryon Aragam
3. Matteo Barigozzi
4. Marc Hallin

Citations in the corpus

• Testing nonparametric and semiparametric hypotheses in vector stationary processes (2008, background): “[54] M. Reale, G. Tunnicliffe Wilson, Identification of vector AR models with recursive structural errors using conditional independence graphs, Statist.”
• DYNOTEARS: Structure Learning from Time-Series Data (2020, background): “There is also an extensive literature on learning SVAR models in the econometrics and statistics literature (Demiralp and Hoover, 2003; Lanne et al., 2017; Reale and Wilson, 2001, 2002; Swanson and Granger, 1997; Tank et al., 2019).”