LASSO-Driven Inference in Time and Space

Chernozhukov, Victor; Härdle, Wolfgang Karl; Huang, Chen; Wang, Weining

doi:10.48550/arxiv.1806.05081

Cited by 3 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above results are similar to Theorem 5.8 of Chernozhukov et al (2018b), which is proved by applying Theorem 5.1 of Zhang and Wu (2017).…”

Section: Simultaneous Inferencesupporting

confidence: 80%

“…holds with probability 1−O(1), where b n (1+u)s C(u) (recall the assumption ii) in Lemma 3.3) for sufficiently large n and u > 0. According to Corollary 5.1 of Chernozhukov et al (2018b), the order of λ n is given by…”

Section: Identificationmentioning

confidence: 99%

“…Chen et al (2019) estimate the system connectedness of a financial network driven by the tail events following a correlation methodology proposed by Ng (2006). Chernozhukov et al (2018b) study the network estimation in a high-dimensional time series model without incorporating the contemporaneous relationships of the firms. Financial networks shall be regarded as a particular kind of complex dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Network with Focally Sparse Structure

Chernozhukov¹,

Huang²,

Wang³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper studies the estimation of network connectedness with focally sparse structure. We try to uncover the network effect with a flexible sparse deviation from a predetermined adjacency matrix. To be more specific, the sparse deviation structure can be regarded as latent or misspecified linkages. To obtain high-quality estimator for parameters of interest, we propose to use a double regularized high-dimensional generalized method of moments (GMM) framework. Moreover, this framework also facilitates us to conduct the inference. Theoretical results on consistency and asymptotic normality are provided with accounting for general spatial and temporal dependency of the underlying data generating processes. Simulations demonstrate good performance of our proposed procedure. Finally, we apply the methodology to study the spatial network effect of stock returns.

show abstract

“…The above results are similar to Theorem 5.8 of Chernozhukov et al (2018b), which is proved by applying Theorem 5.1 of Zhang and Wu (2017).…”

Section: Simultaneous Inferencesupporting

confidence: 80%

Section: Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning Network with Focally Sparse Structure

Chernozhukov¹,

Huang²,

Wang³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a related approach, Javanmard and Montanari (2014), van de Geer et al (2014) and Zhang and Zhang (2014) introduce debiased or desparsified versions of the lasso that achieve uniform validity based on similar principles for IID Gaussian data. Extensions to the time series case include Chernozhukov et al (2019) who provide desparsified simultaneous inference on the parameters in a high-dimensional regression model allowing for temporal and cross-sectional dependency in covariates and error processes; Krampe et al (2018), who introduce bootstrap-based inference for autoregressive time series models based on the desparsification idea, and Hecq et al (2019) who use the post-double-selection procedure of Belloni et al (2014) for constructing uniformly valid Granger causality test in highdimensional VAR models.…”

Section: Introductionmentioning

confidence: 99%

Lasso Inference for High-Dimensional Time Series

Adamek¹,

Smeekes²,

Wilms³

2020

Preprint

View full text Add to dashboard Cite

The desparsified lasso is a high-dimensional estimation method which provides uniformly valid inference. We extend this method to a time series setting under Near-Epoch Dependence (NED) assumptions allowing for non-Gaussian, serially correlated and heteroskedastic processes, where the number of regressors can possibly grow faster than the time dimension. We first derive an oracle inequality for the (regular) lasso, relaxing the commonly made exact sparsity assumption to a weaker alternative, which permits many small but non-zero parameters. The weak sparsity coupled with the NED assumption means this inequality can also be applied to the (inherently misspecified) nodewise regressions performed in the desparsified lasso. This allows us to establish the uniform asymptotic normality of the desparsified lasso under general conditions. Additionally, we show consistency of a long-run variance estimator, thus providing a complete set of tools for performing inference in high-dimensional linear time series models. Finally, we perform a simulation exercise to demonstrate the small sample properties of the desparsified lasso in common time series settings.

show abstract

“…[18] then extended this idea to linear functionals. [17] considered debiased simultaneous inference in a system of high-dimensional regression equations with temporal and cross-sectional dependency based on a uniform robust postselection procedure. [36] proposed Lasso residual-based tests for checking goodness-of-fit in (low-and) high-dimensional linear models.…”

Section: Introductionmentioning

confidence: 99%

IPAD: Stable Interpretable Forecasting with Knockoffs Inference

Fan

Sharifvaghefi

et al. 2019

Journal of the American Statistical Association

View full text Add to dashboard Cite

Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped in the econometric settings. To this end, in this paper we exploit the general framework of model-X knockoffs introduced recently in Candès, Fan, Janson and Lv (2018), which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in that we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the * Yingying Fan is Dean's Associate Professor in Business Administration,

show abstract

LASSO-Driven Inference in Time and Space

Cited by 3 publications

References 44 publications

Learning Network with Focally Sparse Structure

Learning Network with Focally Sparse Structure

Lasso Inference for High-Dimensional Time Series

IPAD: Stable Interpretable Forecasting with Knockoffs Inference

Contact Info

Product

Resources

About