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Bayesian Compressed Vector Autoregressions

Citations

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Cited by:

  1. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
  2. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
  3. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
  4. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
  5. Shabeer Khan & Mirzat Ullah & Mohammad Rahim Shahzad & Uzair Abdullah Khan & Umair Khan & Sayed M. Eldin & Abeer M. Alotaibi, 2022. "Spillover Connectedness among Global Uncertainties and Sectorial Indices of Pakistan: Evidence from Quantile Connectedness Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
  6. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
  7. Rangan Gupta & Chi Keung Marco Lau & Vasilios Plakandaras & Wing-Keung Wong, 2019. "The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2554-2567, January.
  8. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
  9. Florian Huber & Gary Koop & Michael Pfarrhofer, 2020. "Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations," Papers 2002.10274, arXiv.org.
  10. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
  11. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
  12. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
  13. Korobilis, Dimitris & Pettenuzzo, Davide, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," MPRA Paper 100165, University Library of Munich, Germany.
  14. Carlos Carvalho & Jared D. Fisher & Davide Pettenuzzo, 2018. "Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models," Working Papers 123, Brandeis University, Department of Economics and International Business School.
  15. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
  16. Maximilian Böck & Martin Feldkircher & Pierre L. Siklos, 2021. "International Effects of Euro Area Forward Guidance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(5), pages 1066-1110, October.
  17. Crespo Cuaresma, Jesus & Doppelhofer, Gernot & Feldkircher, Martin & Huber, Florian, 2018. "Spillovers from US monetary policy: Evidence from a time-varying parameter GVAR model," Discussion Paper Series in Economics 31/2018, Norwegian School of Economics, Department of Economics.
  18. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
  19. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  20. Lusompa, Amaze, 2019. "Local Projections, Autocorrelation, and Efficiency," MPRA Paper 99856, University Library of Munich, Germany, revised 11 Apr 2020.
  21. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  22. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
  23. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher & Florian Huber, 2019. "Spillovers from US monetary policy: evidence from a time varying parameter global vector auto‐regressive model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 831-861, June.
  24. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
  25. Cross, Jamie, 2019. "On the reduced macroeconomic volatility of the Australian economy: Good policy or good luck?," Economic Modelling, Elsevier, vol. 77(C), pages 174-186.
  26. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
  27. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
  28. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
  29. Gupta, Rangan & Sun, Xiaojin, 2020. "Forecasting economic policy uncertainty of BRIC countries using Bayesian VARs," Economics Letters, Elsevier, vol. 186(C).
  30. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
  31. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
  32. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
  33. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
  34. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  35. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
  36. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
  37. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
  38. Minerva Mukhopadhyay & David B. Dunson, 2020. "Targeted Random Projection for Prediction From High-Dimensional Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1998-2010, December.
  39. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
  40. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
  41. repec:hal:spmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
  42. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
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