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Local Projections Inference with High-dimensional Controls without Sparsity
Local Projections Inference with High-dimensional Controls without Sparsity
Abstract: This paper presents a comprehensive local projections (LP) framework for estimating future responses to current shocks, robust to high-dimensional controls without relying on sparsity assumptions. The approach is applicable to various settings, including impulse response analysis and difference-in-differences (DiD) estimation. While methods like LASSO exist, they often rely on sparsity assumptions—most parameters are exactly zero, limiting their effectiveness in dense data generation processes. I propose a novel technique incorporating high-dimensional covariates in local projections using the Orthogonal Greedy Algorithm with a high-dimensional AIC (OGA+HDAIC) model selection method. This approach offers robustness in both sparse and dense sce- narios, improved interpretability, and more reliable causal inference in local projections. Simulation studies show superior performance in dense and persistent scenarios com- pared to conventional LP and LASSO-based approaches. In the empirical application, applying the proposed method to Acemoglu, Naidu, Restrepo, and Robinson (2019), I show efficiency gains and robustness to a large set of controls. Additionally, I examine the effect of subjective beliefs on economic aggregates, demonstrating robustness to various model specifications.
Keywords: local projection, high-dimensional covariates, double/debiased machine learning
Published/ Forthcoming Papers
Published/ Forthcoming Papers
Inference in High-dimensional Regression Models without Exact or Lp Sparsity
Inference in High-dimensional Regression Models without Exact or Lp Sparsity
The Review of Economics and Statistics, [link] [arXiv] [Replication Data]
Abstract
We propose a new inference method in high-dimensional regression models and high-dimensional IV regression models. The method is shown to be valid without requiring the exact sparsity or Lp sparsity conditions. Simulation studies demonstrate superior performance of this proposed method over those based on the LASSO or the random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.
Work in Progress
Work in Progress
Bounds for Standard Errors from Combined Data
Bounds for Standard Errors from Combined Data
Abstract
We propose a method for constructing upper and lower bounds on the standard error of a parameter estimated from moment conditions obtained across different samples. Both bounds are derived by exploiting sharp distributional bounds. While this distributional characterization is conceptually appealing, the resulting optimization problem is often numerically challenging due to its non-convex nature. To address this practical difficulty, we complement the distributional characterization with two tractable approaches: (1) explicit sharp bounds when no information about correlations is available, and (2) computationally feasible sharp bounds in more general settings with no or partial correlational information. The latter can be obtained by solving a simple semi-definite program. Finally, we demonstrate the usefulness of our method through three empirical applications in macroeconomics and microeconomics.
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