Department of Economics and Business Economics

Uniform inference in highdimensional dynamic panel data models with approximately sparse fixed effects

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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 uniformly valid 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 nonconstant 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.

Original languageEnglish
JournalEconometric Theory
Volume35
Issue2
Pages (from-to)295-359
Number of pages65
ISSN0266-4666
DOIs
Publication statusPublished - Apr 2019

    Research areas

  • CONFIDENCE-INTERVALS, GROWTH, LASSO, QUANTILE REGRESSION, REGIONS, SHRINKAGE, VARIABLE SELECTION

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