Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Efficient semiparametric copula estimation of regression models with endogeneity
AU - Tran, Kien C.
AU - Tsionas, Mike G.
PY - 2022/5/28
Y1 - 2022/5/28
N2 - An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.
AB - An efficient sieve maximum likelihood estimation procedure for regression models with endogenous regressors using a copula-based approach is proposed. Specifically, the joint distribution of the endogenous regressor and the error term is characterized by a parametric copula function evaluated at the nonparametric marginal distributions. The asymptotic properties of the proposed estimator are derived, including semiparametrically efficient property. Monte Carlo simulations reveal that the proposed method performs well in finite samples comparing to other existing methods. An empirical application is presented to demonstrate the usefulness of the proposed approach.
KW - Economics and Econometrics
U2 - 10.1080/07474938.2021.1957284
DO - 10.1080/07474938.2021.1957284
M3 - Journal article
VL - 41
SP - 485
EP - 504
JO - Econometric Reviews
JF - Econometric Reviews
SN - 0747-4938
IS - 5
ER -