Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247559 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-24
Publisher: 
MDPI, Basel
Abstract: 
This work describes a versatile and readily-deployable sensitivity analysis of an ordinary least squares (OLS) inference with respect to possible endogeneity in the explanatory variables of the usual k-variate linear multiple regression model. This sensitivity analysis is based on a derivation of the sampling distribution of the OLS parameter estimator, extended to the setting where some, or all, of the explanatory variables are endogenous. In exchange for restricting attention to possible endogeneity which is solely linear in nature-the most typical case-no additional model assumptions must be made, beyond the usual ones for a model with stochastic regressors. The sensitivity analysis quantifies the sensitivity of hypothesis test rejection p-values and/or estimated confidence intervals to such endogeneity, enabling an informed judgment as to whether any selected inference is "robust" versus "fragile." The usefulness of this sensitivity analysis-as a "screen" for potential endogeneity issues-is illustrated with an example from the empirical growth literature. This example is extended to an extremely large sample, so as to illustrate how this sensitivity analysis can be applied to parameter confidence intervals in the context of massive datasets, as in "big data."
Subjects: 
big data
exogeneity
inference
instrumental variables
large samples
multiple regression
robustness
JEL: 
C2
C15
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





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