Subset selection for vector autoregressive processes using Lasso

Hsu, Nan‐Jung; Hung, Hung-Lin; Chang, Ya-Mei

doi:10.1016/j.csda.2007.12.004

Cited by 182 publications

(139 citation statements)

References 17 publications

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“…Furthermore, Caner and Knight (2010) show that econometricians can use a Bridge estimator to differentiate stationarity from unit root type of nonstationarity and select the optimal lag in autoregression (AR) series as well. The vector autoregression (VAR) case was considered in Hsu, Hung, and Chang (2008). Furthermore, Caner (2009) studied the LASSO method for general generalized method of moments (GMM) estimator also in the case of time series, and Knight (2008) extended the LASSO approach to nearly singular designs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Lasso-Type Estimation for Multivariate Diffusion Processes

Gregorio¹,

Iacus²

2012

Econom. Theory

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The least absolute shrinkage and selection operator (LASSO) is a widely used statistical methodology for simultaneous estimation and variable selection. It is a shrinkage estimation method that allows one to select parsimonious models. In other words, this method estimates the redundant parameters as zero in the large samples and reduces variance of estimates. In recent years, many authors analyzed this technique from a theoretical and applied point of view. We introduce and study the adaptive LASSO problem for discretely observed multivariate diffusion processes. We prove oracle properties and also derive the asymptotic distribution of the LASSO estimator. This is a nontrivial extension of previous results by Wang and Leng (2007, Journal of the American Statistical Association, 102(479), 1039-1048) on LASSO estimation because of different rates of convergence of the estimators in the drift and diffusion coefficients. We perform simulations and real data analysis to provide some evidence on the applicability of this method.

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Section: Introductionmentioning

confidence: 99%

Adaptive Lasso-Type Estimation for Multivariate Diffusion Processes

Gregorio¹,

Iacus²

2012

Econom. Theory

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“…However, these methods investigate only a certain portion of all potential submodels. An alternative is presented by the least absolute shrinkage and selection operator (Lasso) that was initially suggested by Tibshirani (1996) as a constrained version of the ordinary least squares estimator, but later is also applied to VAR models (Hsu et al 2007). Nevertheless, due to the shrinkage parameter the method exhibits a substantial estimation bias (Hsu et al 2007, Savin 2010.…”

Section: Heuristic Algorithm and Resulting Modelsmentioning

confidence: 99%

Heuristic model selection for leading indicators in Russia and Germany

Savin

Winker²

2013

OECD Journal: Journal of Business Cycle Measurement and Analysis

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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“…The case of multivariate time series, particularly vector autoregression, was covered by e.g. Hsu et al (2008).…”

Section: Modification Of Lasso In Quantile Regressionmentioning

confidence: 99%

Time Varying Quantile Lasso

Zbonakova¹,

Wang²

2016

SSRN Journal

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In the present paper we study the dynamics of penalization parameter λ of the least absolute shrinkage and selection operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The dynamic behaviour of the parameter λ can be observed when the model is assumed to vary over time and therefore the fitting is performed with the use of moving windows. The proposal of investigating time series of λ and its dependency on model characteristics was brought into focus by Härdle et al. (2016), which was a foundation of FinancialRiskMeter (http://frm.wiwi.hu-berlin.de). Following the ideas behind the two aforementioned projects, we use the derivation of the formula for the penalization parameter λ as a result of the optimization problem. This reveals three possible effects driving λ; variance of the error term, correlation structure of the covariates and number of nonzero coefficients of the model. Our aim is to disentangle these three effect and investigate their relationship with the tuning parameter λ, which is conducted by a simulation study. After dealing with the theoretical impact of the three model characteristics on λ, empirical application is performed and the idea of implementing the parameter λ into a systemic risk measure is presented. The codes used to obtain the results included in this work are available on

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Subset selection for vector autoregressive processes using Lasso

Cited by 182 publications

References 17 publications

Adaptive Lasso-Type Estimation for Multivariate Diffusion Processes

Adaptive Lasso-Type Estimation for Multivariate Diffusion Processes

Heuristic model selection for leading indicators in Russia and Germany

Time Varying Quantile Lasso

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