IDEAS home Printed from https://ideas.repec.org/r/rim/rimwps/44_14.html
   My bibliography  Save this item

Stochastic Model Specification Search for Time-Varying Parameter VARs

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Annalisa Cadonna & Sylvia Fruhwirth-Schnatter & Peter Knaus, 2019. "Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models," Papers 1912.03100, arXiv.org.
  2. Florian Huber & Gregor Kastner & Martin Feldkircher, 2019. "Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 621-640, August.
  3. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
  4. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
  5. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
  6. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
  7. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
  8. Annalisa Cadonna & Sylvia Frühwirth-Schnatter & Peter Knaus, 2020. "Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models," Econometrics, MDPI, vol. 8(2), pages 1-36, May.
  9. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
  10. Phan, Tuan, 2016. "Has Monetary Policy Become More Aggressive, But Less Effective Over Time?," MPRA Paper 107200, University Library of Munich, Germany.
  11. Hauzenberger Niko & Huber Florian & Koop Gary, 2024. "Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 201-225, April.
  12. Sylvia Fruhwirth-Schnatter & Peter Knaus, 2022. "Sparse Bayesian State-Space and Time-Varying Parameter Models," Papers 2207.12147, arXiv.org.
  13. Dawid J. van Lill, 2017. "Changes in the Liquidity Effect Over Time: Evidence from Four Monetary Policy Regimes," Working Papers 704, Economic Research Southern Africa.
  14. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
  15. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
  16. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2019. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 129-136.
  17. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
  18. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
  19. Reusens Peter & Croux Christophe, 2017. "Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
  20. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
  21. 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.
  22. 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.
  23. Martin Feldkircher & Luis Gruber & Florian Huber & Gregor Kastner, 2024. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2126-2145, September.
  24. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.
  25. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
  26. 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.
  27. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  28. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
  29. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," SIRE Discussion Papers 2014-022, Scottish Institute for Research in Economics (SIRE).
  30. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).
  31. 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.
  32. Liu, Junbin & Liu, Xiaoxing & Shi, Guangping, 2019. "What influences portfolio contagion among open-end mutual funds?," Finance Research Letters, Elsevier, vol. 30(C), pages 145-152.
  33. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
  34. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
  35. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.
  36. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.
  37. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.
  38. Williams Ohemeng & Elvis Kwame Agyapong & Kenneth Ofori-Boateng, 2021. "Exchange rate and inflation dynamics: does the month or quarter of the year matter?," SN Business & Economics, Springer, vol. 1(6), pages 1-24, June.
  39. Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
  40. Michael O’Grady, 2019. "Estimating the Output, Inflation and Unemployment Gaps in Ireland using Bayesian Model Averaging," The Economic and Social Review, Economic and Social Studies, vol. 50(1), pages 35-76.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.