Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/214502
Title: MODEL AVERAGING IN TWO-STAGE LEAST SQUARES
Authors: LORAINE SENG LIPING (SUN LIPING)
ORCID iD:   orcid.org/0000-0001-7328-5367
Keywords: model averaging, two-stage least squares, structural equation model, high-dimensional, instrumental variables, causal inference
Issue Date: 17-Aug-2021
Citation: LORAINE SENG LIPING (SUN LIPING) (2021-08-17). MODEL AVERAGING IN TWO-STAGE LEAST SQUARES. ScholarBank@NUS Repository.
Abstract: Causal inference using observational data is common since randomised controlled trials might not be available. However, producing reliable causal inferences from observational studies is challenging due to possible confounding with unobserved variables. The instrumental variable (IV) methods are attractive since they can lead to a consistent answer to the main question in causal modelling. However, it is acknowledged in the literature that using weak IVs might not suit the inference goal satisfactorily. We consider the problem of estimating causal effects in an observational study, allowing some IVs to be weak. To incorporate them in a 2-stage least squares (2SLS) estimation procedure, we consider a model averaging technique. Theoretical properties are established, including the consistency and asymptotic normality of the estimated causal parameter. Numerical studies are conducted to assess the performance in low- and high-dimensional settings. A real data example on home price is analysed to illustrate our methodology.
URI: https://scholarbank.nus.edu.sg/handle/10635/214502
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
SengLPL.pdf1.04 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

39
checked on Sep 29, 2022

Download(s)

8
checked on Sep 29, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.