Jonas Striaukas
Personal Details
First Name: | Jonas |
Middle Name: | |
Last Name: | Striaukas |
Suffix: | |
RePEc Short-ID: | pst799 |
[This author has chosen not to make the email address public] | |
https://jstriaukas.github.io/ | |
Terminal Degree: | (from RePEc Genealogy) |
Affiliation
Louvain Finance
Louvain Institute of Data Analysis and Modelling in Economics and Statistics (LIDAM)
Université Catholique de Louvain
Louvain-la-Neuve, Belgiumhttps://uclouvain.be/en/research-institutes/lidam/lfin
RePEc:edi:lfuclbe (more details at EDIRC)
Research output
Jump to: Working papersWorking papers
- Weber, Matthias & Striaukas, Jonas & Schumacher, Martin & Binder, Harald, 2021.
"Regularized regression when covariates are linked on a network: the 3CoSE algorithm,"
LIDAM Reprints LFIN
2021022, Université catholique de Louvain, Louvain Finance (LFIN).
- Matthias Weber & Jonas Striaukas & Martin Schumacher & Harald Binder, 2023. "Regularized regression when covariates are linked on a network: the 3CoSE algorithm," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(3), pages 535-554, February.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021.
"Machine Learning Time Series Regressions With an Application to Nowcasting,"
LIDAM Discussion Papers LFIN
2021004, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers 2005.14057, arXiv.org, revised Dec 2020.
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020.
"Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application,"
Papers
2008.03600, arXiv.org, revised Nov 2021.
- Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
- Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
- WEBER Matthias, & STRIAUKAS Jonas, & SCHUMACHER Martin, & HARALD Binder,, 2018.
"Network constrained covariate coefficient and connection sign estimation,"
LIDAM Discussion Papers CORE
2018018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Matthias Weber & Jonas Striaukas & Martin Schumacher & Harald Binder, 2018. "Network constrained covariate coefficient and connection sign estimation," Bank of Lithuania Discussion Paper Series 8, Bank of Lithuania.
- Jonas Striaukas & Martin Schumacher & Harald Binder & Matthias Weber, 2020. "Network-Constrained Covariate Coefficient and Connection Sign Estimation," Working Papers on Finance 2001, University of St. Gallen, School of Finance.
- COMUNALE Mariarosaria & STRIAUKAS Jonas, 2017.
"Unconventional monetary olicy: interest rates and low inflation. A review of literature and methods,"
LIDAM Discussion Papers CORE
2017026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional Monetary Policy: Interest Rates and Low Inflation. A Review of Literature and Methods," Bank of Lithuania Occasional Paper Series 13, Bank of Lithuania.
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional monetary policy: interest rates and low inflation. A review of literature and methods," CEIS Research Paper 406, Tor Vergata University, CEIS, revised 12 May 2017.
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional monetary policy: interest rates and low inflation: A review of literature and methods," CAMA Working Papers 2017-29, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
Citations
Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.Working papers
- Weber, Matthias & Striaukas, Jonas & Schumacher, Martin & Binder, Harald, 2021.
"Regularized regression when covariates are linked on a network: the 3CoSE algorithm,"
LIDAM Reprints LFIN
2021022, Université catholique de Louvain, Louvain Finance (LFIN).
- Matthias Weber & Jonas Striaukas & Martin Schumacher & Harald Binder, 2023. "Regularized regression when covariates are linked on a network: the 3CoSE algorithm," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(3), pages 535-554, February.
Cited by:
- Weber, Matthias, 2022. "From Individual Human Decisions to Economic and Financial Policies," SocArXiv 5ju7z, Center for Open Science.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021.
"Machine Learning Time Series Regressions With an Application to Nowcasting,"
LIDAM Discussion Papers LFIN
2021004, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers 2005.14057, arXiv.org, revised Dec 2020.
Cited by:
- Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022.
"A neural network ensemble approach for GDP forecasting,"
Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
- Luigi Longo & Massimo Riccaboni & Armando Rungi, 2021. "A Neural Network Ensemble Approach for GDP Forecasting," Working Papers 02/2021, IMT School for Advanced Studies Lucca, revised Mar 2021.
- Caroline Jardet & Baptiste Meunier, 2022.
"Nowcasting world GDP growth with high‐frequency data,"
Post-Print
hal-03647097, HAL.
- Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
- Jardet Caroline & Meunier Baptiste, 2020. "Nowcasting World GDP Growth with High-Frequency Data," Working papers 788, Banque de France.
- Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
- Matteo Mogliani & Anna Simoni, 2020.
"Bayesian MIDAS penalized regressions: Estimation, selection, and prediction,"
Post-Print
hal-03089878, HAL.
- Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
- Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
- Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
- Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
- Sarun Kamolthip, 2021.
"Macroeconomic forecasting with LSTM and mixed frequency time series data,"
Papers
2109.13777, arXiv.org.
- Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
- Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
- Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
- Frank Schorfheide & Dongho Song, 2024.
"Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic,"
International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
- Schorfheide, Frank & Song, Dongho, 2021. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," CEPR Discussion Papers 16760, C.E.P.R. Discussion Papers.
- Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers 20-26, Federal Reserve Bank of Philadelphia.
- Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive 20-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Frank Schorfheide & Dongho Song, 2021. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," NBER Working Papers 29535, National Bureau of Economic Research, Inc.
- Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022.
"Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data,"
Papers
2211.00363, arXiv.org, revised Jan 2024.
- Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
- Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020.
"Machine Learning Advances for Time Series Forecasting,"
Papers
2012.12802, arXiv.org, revised Apr 2021.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- Hafner, Christian & Wang, Linqi, 2020.
"Dynamic portfolio selection with sector-specific regularization,"
LIDAM Discussion Papers ISBA
2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints LFIN 2022007, Université catholique de Louvain, Louvain Finance (LFIN).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints ISBA 2022013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Kajal Lahiri & Cheng Yang, 2021.
"Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York,"
CESifo Working Paper Series
9365, CESifo.
- Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
- Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
- Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023.
"Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany,"
Discussion Papers
34/2023, Deutsche Bundesbank.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020.
"Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application,"
Papers
2008.03600, arXiv.org, revised Nov 2021.
- Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
- Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
- Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
- Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.
- Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
- d’Aspremont, Alexandre & Arous, Simon Ben & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2024.
"Satellites turn “concrete”: tracking cement with satellite data and neural networks,"
Working Paper Series
2900, European Central Bank.
- Alexandre Aspremont & Simon Ben Arous & Jean-Charles Bricongne & Benjamin Lietti & Baptiste Meunier, 2023. "Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks," Working papers 916, Banque de France.
- Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
- Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting," Papers 2404.02671, arXiv.org, revised Sep 2024.
- Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023.
"Forecasting Value-at-Risk using deep neural network quantile regression,"
Essex Finance Centre Working Papers
34837, University of Essex, Essex Business School.
- Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
- Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
- Ziwei Mei & Zhentao Shi & Peter C. B. Phillips, 2022.
"The boosted HP filter is more general than you might think,"
Cowles Foundation Discussion Papers
2348, Cowles Foundation for Research in Economics, Yale University.
- Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
- Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
- Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
- Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
- Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024. "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, vol. 40, pages 149-176.
- Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
- Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
- Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
- Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022.
"Testing big data in a big crisis: Nowcasting under COVID-19,"
Working Papers
2022-06, Joint Research Centre, European Commission.
- Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
- Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
- Sander Barendse, 2023. "Expected Shortfall LASSO," Papers 2307.01033, arXiv.org, revised Jan 2024.
- Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
- Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "On LASSO Inference for High Dimensional Predictive Regression," Papers 2409.10030, arXiv.org.
- Anders Bredahl Kock & Rasmus S{o}ndergaard Pedersen & Jesper Riis-Vestergaard S{o}rensen, 2024. "Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions," Papers 2403.06657, arXiv.org.
- Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
- Ajit Desai, 2023.
"Machine learning for economics research: when, what and how,"
Staff Analytical Notes
2023-16, Bank of Canada.
- Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
- Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
- Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
- Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
- Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020.
"Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application,"
Papers
2008.03600, arXiv.org, revised Nov 2021.
- Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
Cited by:
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020.
"Machine Learning Advances for Time Series Forecasting,"
Papers
2012.12802, arXiv.org, revised Apr 2021.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- Hafner, Christian & Wang, Linqi, 2020.
"Dynamic portfolio selection with sector-specific regularization,"
LIDAM Discussion Papers ISBA
2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints LFIN 2022007, Université catholique de Louvain, Louvain Finance (LFIN).
- Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints ISBA 2022013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
- Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019.
"High-Dimensional Granger Causality Tests with an Application to VIX and News,"
Papers
1912.06307, arXiv.org, revised Feb 2021.
Cited by:
- Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020.
"Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice,"
Papers
2010.08463, arXiv.org, revised Nov 2021.
- Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022.
"Machine Learning Time Series Regressions With an Application to Nowcasting,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers 2005.14057, arXiv.org, revised Dec 2020.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Discussion Papers LFIN 2021004, Université catholique de Louvain, Louvain Finance (LFIN).
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
- Robert Adamek & Stephan Smeekes & Ines Wilms, 2020.
"Lasso Inference for High-Dimensional Time Series,"
Papers
2007.10952, arXiv.org, revised Sep 2022.
- Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
- Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020.
"Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application,"
Papers
2008.03600, arXiv.org, revised Nov 2021.
- Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
- Andrii Babii, 2020.
"High-dimensional mixed-frequency IV regression,"
Papers
2003.13478, arXiv.org.
- Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
- Christian Brownlees & Gu{dh}mundur Stef'an Gu{dh}mundsson, 2021. "Performance of Empirical Risk Minimization for Linear Regression with Dependent Data," Papers 2104.12127, arXiv.org, revised May 2023.
- Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
- Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020.
"Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice,"
Papers
2010.08463, arXiv.org, revised Nov 2021.
- Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018.
"Quantile-based Inflation Risk Models,"
Working Paper Research
349, National Bank of Belgium.
Cited by:
- Pfarrhofer, Michael, 2022.
"Modeling tail risks of inflation using unobserved component quantile regressions,"
Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
- Michael Pfarrhofer, 2021. "Modeling tail risks of inflation using unobserved component quantile regressions," Papers 2103.03632, arXiv.org, revised Oct 2021.
- Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022.
"Tail Forecasting with Multivariate Bayesian Additive Regression Trees,"
CEPR Discussion Papers
17461, C.E.P.R. Discussion Papers.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
- Nina Boyarchenko & Domenico Giannone & Anna Kovner, 2020.
"Bank Capital and Real GDP Growth,"
Staff Reports
950, Federal Reserve Bank of New York.
- Nina Boyarchenko & Domenico Giannone & Anna Kovner, 2024. "Bank Capital and Real GDP Growth," Working Paper 24-08, Federal Reserve Bank of Richmond.
- Pfarrhofer, Michael, 2022.
"Modeling tail risks of inflation using unobserved component quantile regressions,"
Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
- COMUNALE Mariarosaria & STRIAUKAS Jonas, 2017.
"Unconventional monetary olicy: interest rates and low inflation. A review of literature and methods,"
LIDAM Discussion Papers CORE
2017026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional Monetary Policy: Interest Rates and Low Inflation. A Review of Literature and Methods," Bank of Lithuania Occasional Paper Series 13, Bank of Lithuania.
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional monetary policy: interest rates and low inflation. A review of literature and methods," CEIS Research Paper 406, Tor Vergata University, CEIS, revised 12 May 2017.
- Mariarosaria Comunale & Jonas Striaukas, 2017. "Unconventional monetary policy: interest rates and low inflation: A review of literature and methods," CAMA Working Papers 2017-29, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
Cited by:
- Eijffinger, Sylvester & Malagon, Jonathan, 2018. "Financial spillovers of international monetary policy: Six hypotheses on the Latin American case, 2010-2016," CEPR Discussion Papers 12678, C.E.P.R. Discussion Papers.
- Jose David GARCIA REVELO & Yannick LUCOTTE & Florian PRADINES-JOBET, 2019. "Macroprudential and Monetary Policies : The Need to Dance the Tango in Harmony," LEO Working Papers / DR LEO 2691, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Sona Benecka & Ludmila Fadejeva & Martin Feldkircher, 2018.
"Spillovers from Euro Area Monetary Policy: A Focus on Emerging Europe,"
Working Papers
2018/2, Czech National Bank.
- Sona Benecka & Ludmila Fadejeva & Martin Feldkircher, 2018. "Spillovers from Euro Area Monetary Policy: A Focus on Emerging Europe," Working Papers 2018/04, Latvijas Banka.
- Mariarosaria Comunale & Francesco Paolo Mongelli, 2021.
"Tracking growth in the euro area subject to a dimensionality problem,"
Applied Economics, Taylor & Francis Journals, vol. 53(57), pages 6611-6625, December.
- Comunale, Mariarosaria & Mongelli, Francesco Paolo, 2021. "Tracking growth in the euro area subject to a dimensionality problem," Working Paper Series 2591, European Central Bank.
- Beckmann, Joscha & Comunale, Mariarosaria, 2021.
"Exchange rate fluctuations and the financial channel in emerging economies,"
BOFIT Discussion Papers
11/2021, Bank of Finland Institute for Emerging Economies (BOFIT).
- Joscha Beckmann & Mariarosaria Comunale, 2020. "Exchange rate fluctuations and the financial channel in emerging economies," Bank of Lithuania Working Paper Series 83, Bank of Lithuania.
- Mariarosaria Comunale & Francesco Paolo Mongelli, 2019.
"Who did it? A European Detective Story. Was it Real, Financial, Monetary and/or Institutional: Tracking Growth in the Euro Area with an Atheoretical Tool,"
Bank of Lithuania Working Paper Series
70, Bank of Lithuania.
- Mariarosaria Comunale & Francesco Paolo Mongelli, 2020. "Who did it? A European detective story was it real, financial, monetary and/or institutional: Tracking growth in the Euro area with an atheoretical tool," CAMA Working Papers 2020-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Mariarosaria Comunale & Francesco Paolo Mongelli, 2020. "Who did it? A European Detective Story Was it Real, Financial, Monetary and/or Institutional: Tracking Growth in the Euro Area with an Atheoretical Tool," CEIS Research Paper 481, Tor Vergata University, CEIS, revised 11 May 2020.
- Mariarosaria Comunale & Francesco Paolo Mongelli, 2019. "Euro Area Growth and European Institutional Reforms," Bank of Lithuania Occasional Paper Series 24, Bank of Lithuania.
- William Gatt & Germano Ruisi, 2022. "The spillover of euro area shocks to the Maltese economy," CBM Working Papers WP/03/2022, Central Bank of Malta.
- Andrea Colabella, 2019. "Do the ECB’s monetary policies benefit emerging market economies? A GVAR analysis on the crisis and post-crisis period," Temi di discussione (Economic working papers) 1207, Bank of Italy, Economic Research and International Relations Area.
- Garcia Revelo, José David & Lucotte, Yannick & Pradines-Jobet, Florian, 2020. "Macroprudential and monetary policies: The need to dance the Tango in harmony," Journal of International Money and Finance, Elsevier, vol. 108(C).
- Sedegah Kordzo & Odhiambo Nicholas M., 2021. "A Review of the Impact of External Shocks on Monetary Policy Effectiveness in Non-WAEMU Countries," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 31(3), pages 37-59, September.
- Rasa Stasiukynaite, 2017. "Understanding Monetary Policy Stance," Bank of Lithuania Occasional Paper Series 14, Bank of Lithuania.
- Benecká, Soňa & Fadejeva, Ludmila & Feldkircher, Martin, 2020. "The impact of euro Area monetary policy on Central and Eastern Europe," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1310-1333.
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NEP Fields
NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.- NEP-MAC: Macroeconomics (7) 2017-04-23 2017-05-21 2017-06-25 2018-01-15 2019-01-21 2020-06-15 2022-02-28. Author is listed
- NEP-ECM: Econometrics (5) 2018-09-17 2019-01-21 2020-01-13 2020-06-15 2020-08-31. Author is listed
- NEP-BIG: Big Data (4) 2020-01-13 2020-06-15 2020-08-31 2022-02-28. Author is listed
- NEP-MON: Monetary Economics (4) 2017-04-23 2017-05-21 2017-06-25 2018-01-15. Author is listed
- NEP-CBA: Central Banking (3) 2017-04-23 2017-05-21 2017-06-25. Author is listed
- NEP-CMP: Computational Economics (3) 2020-06-15 2020-08-31 2022-02-28. Author is listed
- NEP-ETS: Econometric Time Series (2) 2020-01-13 2020-06-15
- NEP-EEC: European Economics (1) 2017-04-23
- NEP-FDG: Financial Development and Growth (1) 2022-02-28
- NEP-FOR: Forecasting (1) 2019-01-21
- NEP-NET: Network Economics (1) 2020-05-04
- NEP-ORE: Operations Research (1) 2019-01-21
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