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- W3124875314 abstract "We apply GMM-Lasso (Caner, 2009) to a linear structural model with many endogenous regressors. If the true parameter is sufficiently sparse, we can establish a new oracle inequality, which implies that GMM-Lasso performs almost as well as if we knew a priori the identities of the relevant variables. Sparsity, meaning that most of the true coefficients are too small to matter, naturally arises in applications where the model is derived from economic theory. In addition, we propose to use a modified version of AIC or BIC to select the tuning parameter in practical implementation. Simulations provide supportive evidence concerning the finite sample properties of the estimator." @default.
- W3124875314 created "2021-02-01" @default.
- W3124875314 creator A5080743390 @default.
- W3124875314 date "2014-01-01" @default.
- W3124875314 modified "2023-09-27" @default.
- W3124875314 title "Estimation of Sparse Structural Parameter with Many Endogenous Variables" @default.
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- W3124875314 doi "https://doi.org/10.2139/ssrn.2513398" @default.
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