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Identification-robust inference for endogeneity parameters in linear structural models
Citation
Doko Tchatoka, F and Dufour, S, Identification-robust inference for endogeneity parameters in linear structural models, The Econometrics Journal, 17, (1) pp. 165-187. ISSN 1368-423X (2014) [Refereed Article]
Copyright Statement
Copyright 2014 the Authors-Copyright 2014 The Econometrics Journal
Abstract
We provide a generalization of the Anderson–Rubin (AR) procedure for inference
on parameters that represent the dependence between possibly endogenous explanatory
variables and disturbances in a linear structural equation (endogeneity parameters). We stress
the distinction between regression and covariance endogeneity parameters. Such parameters
have intrinsic interest (because they measure the effect of latent variables, which induce
simultaneity) and play a central role in selecting an estimation method (such as ordinary leastsquares
or instrumental variable methods). We observe that endogeneity parameters might
not be identifiable and we give the relevant identification conditions. These conditions entail
a simple identification correspondence between regression endogeneity parameters and the
usual structural parameters, while the identification of covariance endogeneity parameters
typically fails as soon as global identification fails. We develop identification-robust finitesample
tests for joint hypotheses involving structural and regression endogeneity parameters,
as well as marginal hypotheses on regression endogeneity parameters. For Gaussian errors,
we provide tests and confidence sets based on standard Fisher critical values. For a wide
class of parametric non-Gaussian errors (possibly heavy-tailed), we show that exact Monte
Carlo procedures can be applied using the statistics considered. As a special case, this result
also holds for usual AR-type tests on structural coefficients. For covariance endogeneity
parameters, we supply an asymptotic (identification-robust) distributional theory. Tests for
partial exogeneity hypotheses (for individual potentially endogenous explanatory variables)
are covered as special cases. The proposed tests are applied to two empirical examples: the
relation between trade and economic growth, and the widely studied problem of returns to
education.
Item Details
Item Type: | Refereed Article |
---|---|
Keywords: | AR-type statistic, Endogeneity, Identification-robust confidence sets, Partial exogeneity test, Projection-based techniques |
Research Division: | Commerce, Management, Tourism and Services |
Research Group: | Business systems in context |
Research Field: | Business systems in context not elsewhere classified |
Objective Division: | Economic Framework |
Objective Group: | Other economic framework |
Objective Field: | Other economic framework not elsewhere classified |
UTAS Author: | Doko Tchatoka, F (Dr Firmin Doko Tchatoka) |
ID Code: | 90592 |
Year Published: | 2014 |
Web of Science® Times Cited: | 9 |
Deposited By: | TSBE |
Deposited On: | 2014-04-11 |
Last Modified: | 2021-07-06 |
Downloads: | 0 |
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