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, Belgium
https://uclouvain.be/en/research-institutes/lidam/lfin
RePEc:edi:lfuclbe (more details at EDIRC)

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

1. 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:

   1. Weber, Matthias, 2022. "From Individual Human Decisions to Economic and Financial Policies," SocArXiv 5ju7z, Center for Open Science.

2. 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 & 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:

   1. 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.
   2. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
      • Jardet Caroline & Meunier Baptiste, 2020. "Nowcasting World GDP Growth with High-Frequency Data," Working papers 788, Banque de France.
      • 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.
   3. 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.
   4. 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.
   5. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
   6. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
      • Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
      • 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 & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
   7. 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.
   8. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org.
   9. 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).
   10. 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.
       • 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, 2021. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," NBER Working Papers 29535, National Bureau of Economic Research, Inc.
   11. 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.
   12. 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.
   13. 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).
       • 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 & 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).
   14. 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.
   15. 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.
   16. 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.
       • 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.
   17. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
   18. 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, November.
   19. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).

3. 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.

   Cited by:

   1. 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.
   2. 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).
      • 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 & 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).
   3. 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.

4. 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:

   1. 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.
      • 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.
   2. 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 Sep 2022.
   3. Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
   4. 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.
      • 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).
   5. 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.
   6. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.

5. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.

   Cited by:

   1. 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.
   2. Nina Boyarchenko & Domenico Giannone & Anna Kovner, 2020. "Bank Capital and Real GDP Growth," Staff Reports 950, Federal Reserve Bank of New York.
   3. Clark, Todd & Huber, Florian & , & 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, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.

6. 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," 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.
   • 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.

   Cited by:

   1. Eijffinger, Sylvester C W & 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.
   2. 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.
   3. 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.
   4. 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.
   5. 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).
   6. 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.
      • 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).
   7. 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.
   8. 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," CEIS Research Paper 481, Tor Vergata University, CEIS, revised 11 May 2020.
      • 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.
   9. 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.
   10. Mariarosaria Comunale & Francesco Paolo Mongelli, 2019. "Euro Area Growth and European Institutional Reforms," Bank of Lithuania Occasional Paper Series 24, Bank of Lithuania.
   11. Rasa Stasiukynaite, 2017. "Understanding Monetary Policy Stance," Bank of Lithuania Occasional Paper Series 14, Bank of Lithuania.
   12. 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.
   13. 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.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

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.

1. 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
2. NEP-ECM: Econometrics (5) 2018-09-17 2019-01-21 2020-01-13 2020-06-15 2020-08-31. Author is listed
3. NEP-BIG: Big Data (4) 2020-01-13 2020-06-15 2020-08-31 2022-02-28. Author is listed
4. NEP-MON: Monetary Economics (4) 2017-04-23 2017-05-21 2017-06-25 2018-01-15. Author is listed
5. NEP-CBA: Central Banking (3) 2017-04-23 2017-05-21 2017-06-25. Author is listed
6. NEP-CMP: Computational Economics (3) 2020-06-15 2020-08-31 2022-02-28. Author is listed
7. NEP-ETS: Econometric Time Series (2) 2020-01-13 2020-06-15
8. NEP-EEC: European Economics (1) 2017-04-23
9. NEP-FDG: Financial Development & Growth (1) 2022-02-28
10. NEP-FOR: Forecasting (1) 2019-01-21
11. NEP-NET: Network Economics (1) 2020-05-04
12. NEP-ORE: Operations Research (1) 2019-01-21

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