Inference in High-dimensional Dynamic Panel Data Models

Anders Kock and Haihan Tang (hht23@cam.ac.uk)
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Haihan Tang: Cambridge University, Postal: Faculty of Economics, Sidgwick Ave, Cambridge, CB3 9DD, UK

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can conduct simultaneous inference on the parameters of the model and construct a uniformly valid estimator of the asymptotic covariance matrix which is robust to conditional heteroskedasticity in the error terms. Allowing for conditional heteroskedasticity is important in dynamic models as the conditional error variance may be non-constant over time and depend on the covariates. Furthermore, our procedure allows for inference on high-dimensional subsets of the parameter vector of an increasing cardinality. We show that the confidence bands resulting from our procedure are asymptotically honest and contract at the optimal rate. This rate is different for the fixed effects than for the remaining parts of the parameter vector.

Keywords: Panel data Dynamic models; Lasso; Desparsification; High-dimensional data; Uniform inference; Honest inference; Oracle inequality; Confidence intervals; Tests (search for similar items in EconPapers)
JEL-codes: C13 C23 C55 (search for similar items in EconPapers)
Pages: 48
Date: 2014-12-30
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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