Generalized covariance‐based inference for models set‐identified from independence restrictions

Christian Gourieroux and Joann Jasiak

Journal of Time Series Analysis, 2025, vol. 46, issue 2, 300-324

Abstract: This article develops statistical inference methods for a class of set‐identified models, where the errors are known functions of observations and the parameters satisfy either serial or/and cross‐sectional independence conditions. This class of models includes the independent component analysis (ICA), Structural Vector Autoregressive (SVAR), and multi‐variate mixed causal–non‐causal models. We use the Generalized Covariance (GCov) estimator to compute the residual‐based portmanteau statistic for testing the error independence hypothesis. Next, we build the confidence sets for the identified sets of parameters by inverting the test statistic. We also discuss the choice (design) of these statistics. The approach is illustrated by simulations examining the under‐identification condition in an ICA model and an application to financial return series.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jtsa.12779

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:bla:jtsera:v:46:y:2025:i:2:p:300-324

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().