EconPapers    
Economics at your fingertips  
 

A Geometric Approach to Nonlinear Econometric Models

Isaiah Andrews and Anna Mikusheva

Econometrica, 2016, vol. 84, 1249-1264

Abstract: Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced‐form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studying the problem of testing a “curved null” in a finite‐sample Gaussian model. Using the curvature of the model, we develop new finite‐sample bounds on the distribution of minimum‐distance statistics. These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://hdl.handle.net/

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:wly:emetrp:v:84:y:2016:i::p:1249-1264

Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues

Access Statistics for this article

Econometrica is currently edited by Guido W. Imbens

More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-03-20
Handle: RePEc:wly:emetrp:v:84:y:2016:i::p:1249-1264