Weak Identification with Bounds in a Class of Minimum Distance Models

Fuente: arXiv
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1. Verfasser: Cox, Gregory Fletcher
Format: Preprint
Veröffentlicht: 2020
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author Cox, Gregory Fletcher
author_facet Cox, Gregory Fletcher
contents When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. This paper demonstrates the value of the bounds and identification-robust inference in a simple latent factor model and a simple GARCH model. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.
format Preprint
id arxiv_https___arxiv_org_abs_2012_11222
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Weak Identification with Bounds in a Class of Minimum Distance Models
Cox, Gregory Fletcher
Econometrics
Statistics Theory
When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. This paper demonstrates the value of the bounds and identification-robust inference in a simple latent factor model and a simple GARCH model. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.
title Weak Identification with Bounds in a Class of Minimum Distance Models
topic Econometrics
Statistics Theory
url https://arxiv.org/abs/2012.11222