Three essays on time-varying parameters and time series networks
Author
Abstract
Suggested Citation
Download full text from publisher
To our knowledge, this item is not available for download. To find whether it is available, there are three options:1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
References listed on IDEAS
- Yu, Ping & Phillips, Peter C.B., 2018.
"Threshold regression with endogeneity,"
Journal of Econometrics, Elsevier, vol. 203(1), pages 50-68.
- Ping Yu & Peter C.B. Phillips, 2014. "Threshold Regression with Endogeneity," Cowles Foundation Discussion Papers 1966, Cowles Foundation for Research in Economics, Yale University.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Avagyan, Vahe & Nogales, Francisco J., 2015. "D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties," DES - Working Papers. Statistics and Econometrics. WS 21775, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Lam, Clifford, 2008. "Estimation of large precision matrices through block penalization," LSE Research Online Documents on Economics 31543, London School of Economics and Political Science, LSE Library.
- Giraud Christophe & Huet Sylvie & Verzelen Nicolas, 2012. "Graph Selection with GGMselect," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-52, February.
- de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018.
"Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition,"
CEPR Discussion Papers
12792, C.E.P.R. Discussion Papers.
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
- Aureo de Paula & Imran Rasul & Pedro Souza, 2019. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," Papers 1910.07452, arXiv.org, revised Oct 2023.
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: theory and an application to tax competition," IFS Working Papers WCWP21/23, Institute for Fiscal Studies.
- Imran Rasul & Pedro Souza & Aureo de Paula, 2023. "Identifying Network Ties from Panel Data: Theory and an application to tax competition," POID Working Papers 081, Centre for Economic Performance, LSE.
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: theory and an application to tax competition," CeMMAP working papers 02/23, Institute for Fiscal Studies.
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2019. "Identifying network ties from panel data: theory and an application to tax competition," CeMMAP working papers CWP55/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Lichun Wang & Yuan You & Heng Lian, 2015. "Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models," Statistical Papers, Springer, vol. 56(3), pages 819-828, August.
- Pei Wang & Shunjie Chen & Sijia Yang, 2022. "Recent Advances on Penalized Regression Models for Biological Data," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
- Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.
- Bailey, Natalia & Pesaran, M. Hashem & Smith, L. Vanessa, 2019.
"A multiple testing approach to the regularisation of large sample correlation matrices,"
Journal of Econometrics, Elsevier, vol. 208(2), pages 507-534.
- Natalia Bailey & Vanessa Smith & M. Hashem Pesaran, 2014. "A multiple testing approach to the regularisation of large sample correlation matrices," Cambridge Working Papers in Economics 1413, Faculty of Economics, University of Cambridge.
- Natalia Bailey & M. Hashem Pesaran & L. Vanessa Smith, 2015. "A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices," Working Papers 764, Queen Mary University of London, School of Economics and Finance.
- Natalia Bailey & M. Hashem Pesaran & L. Vanessa Smith, 2014. "A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices," CESifo Working Paper Series 4834, CESifo.
- Chatterjee, A. & Gupta, S. & Lahiri, S.N., 2015. "On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property," Journal of Econometrics, Elsevier, vol. 186(2), pages 317-324.
- Charbonnier Camille & Chiquet Julien & Ambroise Christophe, 2010. "Weighted-LASSO for Structured Network Inference from Time Course Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-29, February.
- Tan, Kean Ming & Witten, Daniela & Shojaie, Ali, 2015. "The cluster graphical lasso for improved estimation of Gaussian graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 23-36.
- Lichun Wang & Peng Lai & Heng Lian, 2013. "Polynomial spline estimation for generalized varying coefficient partially linear models with a diverging number of components," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1083-1103, November.
- Hirose, Kei & Fujisawa, Hironori & Sese, Jun, 2017. "Robust sparse Gaussian graphical modeling," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 172-190.
- Zhang Haixiang & Zheng Yinan & Yoon Grace & Zhang Zhou & Gao Tao & Joyce Brian & Zhang Wei & Schwartz Joel & Vokonas Pantel & Colicino Elena & Baccarelli Andrea & Hou Lifang & Liu Lei, 2017. "Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 159-171, August.
- Lee, Wonyul & Liu, Yufeng, 2012. "Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 241-255.
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018.
"Recovering social networks from panel data: identification, simulations and an application,"
CeMMAP working papers
CWP58/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Aureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: Identification, simulations and an application," Documentos de Trabajo 16173, The Latin American and Caribbean Economic Association (LACEA).
- Áureo de Paula & Imran Rasul & Pedro CL Souza, 2018. "Recovering social networks from panel data: identification, simulations and an application," CeMMAP working papers CWP17/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Ã ureo de Paula & Imran Rasul & Pedro Souza, 2018. "Recovering Social Networks from Panel Data: Identification, Simulations and an Application," Working Papers 2018-013, Human Capital and Economic Opportunity Working Group.
- Ziqi Chen & Chenlei Leng, 2016. "Dynamic Covariance Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1196-1207, July.
- Shan, Liang & Kim, Inyoung, 2018. "Joint estimation of multiple Gaussian graphical models across unbalanced classes," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 89-103.
- Yang, Yihe & Dai, Hongsheng & Pan, Jianxin, 2023. "Block-diagonal precision matrix regularization for ultra-high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
- Taha Alshaybawee & Habshah Midi & Rahim Alhamzawi, 2017. "Bayesian elastic net single index quantile regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 853-871, April.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2018-04-02 (Econometrics)
- NEP-ETS-2018-04-02 (Econometric Time Series)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tiu:tiutis:fc7a10c0-7eee-479a-ac22-b36ce45f2037. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/about/schools/economics-and-management/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.