Time series graphical lasso and sparse VAR estimation

Dallakyan, Aramayis; Kim, Rakheon; Pourahmadi, Mohsen

doi:10.1016/j.csda.2022.107557

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…For future work, I consider a possible extension of the command to implement the time-series Glasso (Dallakyan, Kim, and Pourahmadi Forthcoming) and joint Glasso (Danaher, Wang, and Witten 2014) algorithms.…”

Section: Discussionmentioning

confidence: 99%

graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices

Dallakyan

2022

The Stata Journal: Promoting communications on statistics and S

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In modern multivariate statistics, where high-dimensional datasets are ubiquitous, learning large (inverse-) covariance matrices is imperative for data analysis. A popular approach to estimating a large inverse-covariance matrix is to regularize the Gaussian log-likelihood function by imposing a convex penalty function. In a seminal article, Friedman, Hastie, and Tibshirani (2008, Biostatistics 9: 432–441) proposed a graphical lasso (Glasso) algorithm to efficiently estimate sparse inverse-covariance matrices from the convex regularized log-likelihood function. In this article, I first explore the Glasso algorithm and then introduce a new graphiclasso command for the large inverse-covariance matrix estimation. Moreover, I provide a useful command for tuning parameter selection in the Glasso algorithm using the extended Bayesian information criterion, the Akaike information criterion, and cross-validation. I demonstrate the use of Glasso using simulation results and real-world data analysis.

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Section: Discussionmentioning

confidence: 99%

graphiclasso: Graphical lasso for learning sparse inverse-covariance matrices

Dallakyan

2022

The Stata Journal: Promoting communications on statistics and S

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“…VAR approach was adopted because it can effectively analyze the dynamic effects of shocks of random standard deviation size on the system variables [18,19]. Therefore, this paper used STATA software to construct a VAR model of the Russia-Ukraine conflict and NATO allies' military spending to investigate whether the impact of the Russia-Ukraine conflict on NATO's defense budget increase is persistent.…”

Section: Models and Variablesmentioning

confidence: 99%

Persistence Analysis of the Impact of the Russia-Ukraine Conflict on NATO Allies' Military Spending-Empirical Analysis Based on Vector Autoregressive Model (VAR)

Lyu¹,

Wang²,

Zhang³

et al. 2022

IJSSE

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This paper focused on the sustainability of the impact of the Russia-Ukraine conflict on the military spending of NATO allies. As Russia launched a "special military operation" in Ukraine in February 2022, NATO allies announced their intention to significantly increase military spending in response to the "threat" from Russia. However, under the stimulation of the Russia-Ukraine conflict, it is uncertain whether the NATO defense budget increase spree can be sustained. Based on this, this paper analyzed the sustainability of NATO's military spending increase in the context of the Russia-Ukraine conflict by building a vector autoregressive model (VAR). Through the impulse response analysis, this paper concluded that the Russia-Ukraine conflict shock stimulates NATO allies to increase military spending persistently only for about three years.

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