Lasso Inference for High-Dimensional Time Series

DOI10.48550/arXiv.2007.10952arXiv2007.10952OpenAlexW3125779044MaRDI QIDQ95760

Stephan Smeekes, Robert Adamek, Ines Wilms, Robert Adamek, Ines Wilms, Stephan Smeekes

Publication date: 21 July 2020

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.10952

zbMATH Keywords

time serieshigh-dimensional datalassohonest inference

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

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Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• Regularized estimation in sparse high-dimensional time series models
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Estimation and testing under sparsity. École d'Été de Probabilités de Saint-Flour XLV -- 2015
• Sparse estimators and the oracle property, or the return of Hodges' estimator
• Exact post-selection inference, with application to the Lasso
• Forecasting using a large number of predictors: is Bayesian shrinkage a valid alternative to principal components?
• Valid post-selection inference
• Oracle inequalities for high dimensional vector autoregressions
• Asymptotics of the principal components estimator of large factor models with weakly influential factors
• Statistics for high-dimensional data. Methods, theory and applications.
• Autoregressive process modeling via the Lasso procedure
• On the range of validity of the autoregressive sieve bootstrap
• Lasso-driven inference in time and space
• \(\ell_1\)-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Least angle and \(\ell _{1}\) penalized regression: a review
• Lasso-type recovery of sparse representations for high-dimensional data
• Subset selection for vector autoregressive processes using Lasso
• GARCH (1,1) processes are near epoch dependent
• Asymptotics for linear processes
• A maximal inequality and dependent strong laws
• Gaussian approximation for high dimensional time series
• High-dimensional simultaneous inference with the bootstrap
• Gaussian approximation for high dimensional vector under physical dependence
• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Establishing conditions for the functional central limit theorem in nonlinear and semiparametric time series processes.
• Bootstrap based inference for sparse high-dimensional time series models
• A justification of conditional confidence intervals
• Uniformly valid confidence intervals post-model-selection
• Lasso guarantees for \(\beta \)-mixing heavy-tailed time series
• On the asymptotic variance of the debiased Lasso
• Comparison and anti-concentration bounds for maxima of Gaussian random vectors
• Simultaneous analysis of Lasso and Dantzig selector
• Sparsity oracle inequalities for the Lasso
• Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
• Boosting for high-dimensional linear models
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• Extended BIC for small-n-large-P sparse GLM
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Non-strong mixing autoregressive processes
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Asymptotic Statistics
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• Program Evaluation and Causal Inference With High-Dimensional Data
• A Survey of L₁ Regression
• Inference on Causal and Structural Parameters using Many Moment Inequalities
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Double/debiased machine learning for treatment and structural parameters
• Regularized estimation of high‐dimensional vector autoregressions with weakly dependent innovations
• BLOCK BOOTSTRAP HAC ROBUST TESTS: THE SOPHISTICATION OF THE NAIVE BOOTSTRAP
• Nonlinear system theory: Another look at dependence
• MODEL SELECTION AND INFERENCE: FACTS AND FICTION
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• Performance bounds for parameter estimates of high-dimensional linear models with correlated errors

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