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. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment … or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and … heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression …
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delta method, and (3) provide results for sparsity-based estimation of regression functions for function-valued outcomes. …
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We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high dimensional setting. The setting allows the number of time varying regressors to be larger than the sample size. To make informative estimation and inference feasible,...
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High-dimensional linear models with endogenous variables play an increasingly important role in recent econometric literature. In this work we allow for models with many endogenous variables and many instrument variables to achieve identification. Because of the high-dimensionality in the second...
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This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso …, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the …
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