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In this paper we develop valid inference for high-dimensional time series.
Granger causality testing in high-dimensional VARs: a post-double-selection procedure
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Sample splitting and weak assumption inference for time series
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Online debiasing for adaptively collected high-dimensional data with applications to time series analysis
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High-dimensional Granger causality tests with an application to VIX and news
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Heteroskedasticity and autocorrelation consistent covariance matrix estimation
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Asymptotics for linear processes
Phillips, P. C. B. and V. Solo (1992) · 1992
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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · 1996
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Asymptotic Statistics
Van der Vaart, A. W. (2000) · 2000
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Casella, G. and R. L. Berger (2002) · 2002
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Persistence in high-dimensional linear predictor selection and the virtue of overparametrization
Greenshtein, E. and Y. Ritov (2004) · 2004
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Estimation and inference of impulse responses by local projections
Jordà, Ò. (2005) · 2005
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Leeb, H. and B. M. Pötscher (2005) · 2005
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Nonlinear system theory: Another look at dependence
Wu, W. B. (2005) · 2005
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Boosting for high-dimensional linear models
Bühlmann, P. (2006) · 2006
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Sparsity oracle inequalities for the Lasso
Bunea, F., A. Tsybakov, and M. Wegkamp (2007) · 2007
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Matrix Algebra
Gentle, J. E. (2007) · 2007
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Regression coefficient and autoregressive order shrinkage and selection via the lasso
Wang, H., G. Li, and C.-L. Tsai (2007) · 2007
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Large dimensional factor analysis
Bai, J. and S. Ng (2008) · 2008
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Forecasting using a large number of predictors: Is bayesian shrinkage a valid alternative to principal components?
De Mol, C., D. Giannone, and L. Reichlin (2008) · 2008
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Least angle and ℓ 1 \ell_{1} penalized regression: A review
Hesterberg, T., N. H. Choi, L. Meier, and C. Fraley (2008) · 2008
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Subset selection for vector autoregressive processes using lasso
Hsu, N.-J., H.-L. Hung, and Y.-M. Chang (2008) · 2008
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Adaptive lasso for sparse high-dimensional regression models
Huang, J., S. Ma, and C.-H. Zhang (2008) · 2008
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Sparse estimators and the oracle property, or the return of the Hodges’ estimator
Leeb, H. and B. M. Pötscher (2008) · 2008
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The sparsity and bias of the lasso selection in high-dimensional linear regression
Zhang, C.-H. and J. Huang (2008) · 2008
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Simultaneous analysis of lasso and dantzig selector
Bickel, P. J., Y. Ritov, and A. B. Tsybakov (2009) · 2009
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On uniform deviations of general empirical risks with unboundedness, dependence, and high dimensionality
Jiang, W. (2009) · 2009
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Lasso-type recovery of sparse representations for high-dimensional data
Meinshausen, N. and B. Yu (2009) · 2009
Comparison and anti-concentration bounds for maxima of Gaussian random vectors
Chernozhukov, V., D. Chetverikov, and K. Kato (2015) · 2015
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Valid post-selection and post-regularization inference: an elementary, general approach
Chernozhukov, V., C. Hansen, and M. Spindler (2015) · 2015
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Optimal inference after model selection
Fithian, W., D. Sun, and J. Taylor (2015) · 2015
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Statistical Learning with Sparsity: The Lasso and Generalizations
Hastie, T., R. Tibshirani, and M. Wainwright (2015) · 2015
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Oracle inequalities for high dimensional vector autoregressions
Kock, A. B. and L. Callot (2015) · 2015
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Exact post-selection inference, with application to the lasso
Lee, J. D., D. L. Sun, Y. Sun, and J. E. Taylor (2016) · 2016
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GARCH Models: Structure, Statistical Inference and Financial Applications
Francq, C. and J.-M. Zakoïan (2010) · 2010
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Regularization paths for generalized linear models via coordinate descent
Friedman, J. H., T. Hastie, and R. Tibshirani (2010) · 2010
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Statistics for High-Dimensional Data: Methods, Theory and Applications
Bühlmann, P. and S. van De Geer (2011) · 2011
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Gonçalves, S. and T. J. Vogelsang (2011) · 2011
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Fred-md: A monthly database for macroeconomic research
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ℓ 1 \ell_{1} -regularization of high-dimensional time-series models with non-gaussian and heteroskedastic errors
Medeiros, M. C. and E. F. Mendes (2016) · 2016
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Estimation and Testing under Sparsity
van de Geer, S. A. (2016) · 2016
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Performance bounds for parameter estimates of high-dimensional linear models with correlated errors
Wu, W.-B. and Y. N. Wu (2016) · 2016
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Program evaluation and causal inference with high-dimensional data
Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) · 2017
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High-dimensional simultaneous inference with the bootstrap
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Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins (2018) · 2018
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Gaussian approximation for high dimensional vector under physical dependence
Zhang, X. and G. Cheng (2018) · 2018
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On the asymptotic variance of the debiased lasso
van de Geer, S. (2019) · 2019
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High-Dimensional Probability
Vershynin, R. (2019) · 2019
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Uniformly valid confidence intervals post-model-selection
Bachoc, F., D. Preinerstorfer, and L. Steinberger (2020) · 2020
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Lasso guarantees for β \beta -mixing heavy-tailed time series
Wong, K. C., Z. Li, and A. Tewari (2020) · 2020
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A justification of conditional confidence intervals
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Central limit theorems for high dimensional dependent data
Chang, J., X. Chen, and M. Wu (2021) · 2021
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LASSO-driven inference in time and space
Chernozhukov, V., W. K. Härdle, C. Huang, and W. Wang (2021) · 2021
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Identification through sparsity in factor models: The ℓ 1 \ell_{1} -rotation criterion
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Bootstrap based inference for sparse high-dimensional time series models
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Regularized estimation of high-dimensional vector autoregressions with weakly dependent innovations
Masini, R. P., M. C. Medeiros, and E. F. Mendes (2022) · 2022
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