Boosted p-Values for High-Dimensional Vector Autoregression
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
- Ryan J. Tibshirani & Jonathan Taylor & Richard Lockhart & Robert Tibshirani, 2016. "Exact Post-Selection Inference for Sequential Regression Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 600-620, April.
- Lisha Chen & Jianhua Z. Huang, 2012. "Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1533-1545, December.
- Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
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.- Zhu, Ke & Liu, Hanzhong, 2022. "Confidence intervals for parameters in high-dimensional sparse vector autoregression," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018.
"High-dimensional econometrics and regularized GMM,"
CeMMAP working papers
CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
- Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023.
"Vector autoregression models with skewness and heavy tails,"
Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
- Sune Karlsson & Stepan Mazur & Hoang Nguyen, 2021. "Vector autoregression models with skewness and heavy tails," Papers 2105.11182, arXiv.org.
- Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2021. "Vector autoregression models with skewness and heavy tails," Working Papers 2021:8, Örebro University, School of Business.
- Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 59-67, March.
- Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
- Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "Rejoinder on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 751-758, December.
- Nicolas Städler & Sach Mukherjee, 2017. "Two-sample testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 225-246, January.
- Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
- Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019.
"Bayesian compressed vector autoregressions,"
Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
- Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 2016_09, Business School - Economics, University of Glasgow.
- Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
- Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2017. "Bayesian Compressed Vector Autoregressions," Working Paper series 17-32, Rimini Centre for Economic Analysis.
- Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103R, Brandeis University, Department of Economics and International Business School, revised Apr 2016.
- Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019.
"Bayesian nonparametric sparse VAR models,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
- Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
- Cubadda, Gianluca & Guardabascio, Barbara, 2019.
"Representation, estimation and forecasting of the multivariate index-augmented autoregressive model,"
International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
- Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
- Xue Wu & Chixiang Chen & Zheng Li & Lijun Zhang & Vernon M. Chinchilli & Ming Wang, 2024. "A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 863-883, July.
- Lusompa, Amaze, 2019.
"Local Projections, Autocorrelation, and Efficiency,"
MPRA Paper
99856, University Library of Munich, Germany, revised 11 Apr 2020.
- Amaze Lusompa, 2021. "Local Projections, Autocorrelation, and Efficiency," Research Working Paper RWP 21-01, Federal Reserve Bank of Kansas City.
- Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
- The Tien Mai, 2023. "Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing," Mathematics, MDPI, vol. 11(9), pages 1-13, May.
- Mengheng Li & Ivan Mendieta-Munoz, 2019.
"The multivariate simultaneous unobserved components model and identification via heteroskedasticity,"
Working Paper Series
2019/08, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
- Ivan Mendieta-Munoz & Mengheng Li, 2019. "The Multivariate Simultaneous Unobserved Compenents Model and Identification via Heteroskedasticity," Working Paper Series, Department of Economics, University of Utah 2019_06, University of Utah, Department of Economics.
- Uhrin, Gábor B. & Herwartz, Helmut, 2016. "Monetary policy shocks, set-identifying restrictions, and asset prices: A benchmarking approach for analyzing set-identified models," University of Göttingen Working Papers in Economics 295, University of Goettingen, Department of Economics.
- Forni, Mario & Gambetti, Luca & Lippi, Marco & Sala, Luca, 2020.
"Common Component Structural VARs,"
CEPR Discussion Papers
15529, C.E.P.R. Discussion Papers.
- Mario Forni & Luca Gambetti & marco Lippi & Luca Sala, 2020. "Common Components Structural VARs," Center for Economic Research (RECent) 147, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Sardy, Sylvain & Diaz-Rodriguez, Jairo & Giacobino, Caroline, 2022. "Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
- Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023.
"Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure,"
Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
- Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2022-12-05 (Econometrics)
- NEP-ETS-2022-12-05 (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:arx:papers:2211.02215. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.