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Monte Carlo Smoothing for Nonlinear Time Series

Citations

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Cited by:

  1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
  2. Creal, Drew D. & Wu, Jing Cynthia, 2015. "Estimation of affine term structure models with spanned or unspanned stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 60-81.
  3. Smith, J.Q. & Santos, Antonio A.F., 2006. "Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 329-337, July.
  4. Andrea Beccarini, 2016. "Bias correction through filtering omitted variables and instruments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 754-766, March.
  5. Ioannis Bournakis & Mike Tsionas, 2024. "A Non‐parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 641-671, June.
  6. Dunham, Kylee & Grand, James B., 2016. "Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation," Ecological Modelling, Elsevier, vol. 340(C), pages 28-36.
  7. Born, Benjamin & Pfeifer, Johannes, 2014. "Policy risk and the business cycle," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 68-85.
  8. Delis, Manthos D. & Kazakis, Pantelis & Zopounidis, Constantin, 2023. "Management and takeover decisions," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1256-1268.
  9. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(4), pages 1059-1087.
  10. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
  11. Laurent-Emmanuel Calvet & Veronika Czellar, 2011. "State-Observation Sampling and the Econometrics of Learning Models," Working Papers hal-00625500, HAL.
  12. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
  13. Scharth, Marcel & Kohn, Robert, 2016. "Particle efficient importance sampling," Journal of Econometrics, Elsevier, vol. 190(1), pages 133-147.
  14. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
  15. Xudan Chen & Guoxun Ji & Xinli Sun & Zhen Li, 2019. "Inverse Gaussian–based model with measurement errors for degradation analysis," Journal of Risk and Reliability, , vol. 233(6), pages 1086-1098, December.
  16. Elmar Mertens & James M. Nason, 2020. "Inflation and professional forecast dynamics: An evaluation of stickiness, persistence, and volatility," Quantitative Economics, Econometric Society, vol. 11(4), pages 1485-1520, November.
  17. Manthos D. Delis & Pantelis Kazakis & Constantin Zopounidis, 2021. "Management Practices and Takeover Decisions," Working Papers 2021_10, Business School - Economics, University of Glasgow.
  18. Kim, Jaeho, 2015. "Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market," MPRA Paper 67153, University Library of Munich, Germany.
  19. Rimstad, Kjartan & Omre, Henning, 2013. "Approximate posterior distributions for convolutional two-level hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 187-200.
  20. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2008. "How Structural Are Structural Parameters?," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 83-137, National Bureau of Economic Research, Inc.
  21. Linlin Niu, 2013. "An Affine Term Structure Model with Auxiliary Stochastic Volatility-Covolatility," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
  22. Di Zhang & Qiang Niu & Youzhou Zhou, 2022. "Modeling Randomly Walking Volatility with Chained Gamma Distributions," Papers 2207.01151, arXiv.org, revised Oct 2022.
  23. 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.
  24. Tsionas, Mike G., 2020. "On a model of environmental performance and technology gaps," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1141-1152.
  25. Michael Patrick Curran & Adnan Velic, 2017. "Interest Rate Volatility And Macroeconomic Dynamics: A Cross-Country Analysis," Villanova School of Business Department of Economics and Statistics Working Paper Series 35, Villanova School of Business Department of Economics and Statistics.
  26. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
  27. Rutger Jan Lange, 2020. "Bellman filtering for state-space models," Tinbergen Institute Discussion Papers 20-052/III, Tinbergen Institute, revised 19 May 2021.
  28. Giuliano De Rossi, 2010. "Maximum Likelihood Estimation of the Cox–Ingersoll–Ross Model Using Particle Filters," Computational Economics, Springer;Society for Computational Economics, vol. 36(1), pages 1-16, June.
  29. Anzuini, Alessio & Rossi, Luca & Tommasino, Pietro, 2020. "Fiscal policy uncertainty and the business cycle: Time series evidence from Italy," Journal of Macroeconomics, Elsevier, vol. 65(C).
  30. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
  31. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
  32. Mike G. Tsionas & Nicholas Apergis, 2023. "Another look at contagion across United States and European financial markets: Evidence from the credit default swaps markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1137-1155, January.
  33. Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
  34. Charles S. Bos, 2008. "Model-based Estimation of High Frequency Jump Diffusions with Microstructure Noise and Stochastic Volatility," Tinbergen Institute Discussion Papers 08-011/4, Tinbergen Institute.
  35. Chau, Thi Tuyet Trang & Ailliot, Pierre & Monbet, Valérie, 2021. "An algorithm for non-parametric estimation in state–space models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
  36. Carlos Carvalho & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2023. "Anchored Inflation Expectations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 1-47, January.
  37. Lange, Rutger-Jan, 2024. "Bellman filtering and smoothing for state–space models," Journal of Econometrics, Elsevier, vol. 238(2).
  38. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  39. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
  40. Martin Møller Andreasen, 2008. "Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter," CREATES Research Papers 2008-33, Department of Economics and Business Economics, Aarhus University.
  41. Deschamps, P., 2015. "Alternative Formulation of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors," LIDAM Discussion Papers CORE 2015020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  42. Matteo Cacciatore & Federico Ravenna, 2021. "Uncertainty, Wages and the Business Cycle," The Economic Journal, Royal Economic Society, vol. 131(639), pages 2797-2823.
  43. Doh, Taeyoung, 2011. "Yield curve in an estimated nonlinear macro model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1229-1244, August.
  44. Marco J. Lombardi & Simon J. Godsill, 2004. "On-line Bayesian estimation of AR signals in symmetric alpha-stable noise," Econometrics Working Papers Archive wp2004_05, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  45. Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Optimized Particle Filter," CREATES Research Papers 2010-05, Department of Economics and Business Economics, Aarhus University.
  46. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
  47. Lemoine, M. & Mougin, C., 2010. "The Growth-Volatility Relationship: New Evidence Based on Stochastic Volatility in Mean Models," Working papers 285, Banque de France.
  48. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
  49. Matti Vihola & Jouni Helske & Jordan Franks, 2020. "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1339-1376, December.
  50. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
  51. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
  52. Cheng, Jing & Chan, Ngai Hang, 2019. "Efficient inference for nonlinear state space models: An automatic sample size selection rule," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 143-154.
  53. Pratiti Chatterjee & David Gunawan & Robert Kohn, 2020. "The Interaction Between Credit Constraints and Uncertainty Shocks," Papers 2004.14719, arXiv.org.
  54. Jeongeun Kim & David S. Stoffer, 2008. "Fitting Stochastic Volatility Models in the Presence of Irregular Sampling via Particle Methods and the EM Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 811-833, September.
  55. Tomiyuki Kitamura, 2010. "Measuring Monetary Policy Under Zero Interest Rates With a Dynamic Stochastic General Equilibrium Model: An Application of a Particle Filter," Bank of Japan Working Paper Series 10-E-10, Bank of Japan.
  56. Angel L. Cedeño & Rodrigo A. González & Boris I. Godoy & Rodrigo Carvajal & Juan C. Agüero, 2023. "On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
  57. Matthew Klepacz, 2018. "Price Setting and Volatility: Evidence from Oil Price Volatility Shocks," 2018 Meeting Papers 145, Society for Economic Dynamics.
  58. 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.
  59. Jiawen Xu & Pierre Perron, 2023. "Forecasting in the presence of in-sample and out-of-sample breaks," Empirical Economics, Springer, vol. 64(6), pages 3001-3035, June.
  60. repec:wyi:journl:002173 is not listed on IDEAS
  61. Fredrik Lindsten & Randal Douc & Eric Moulines, 2015. "Uniform Ergodicity of the Particle Gibbs Sampler," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 775-797, September.
  62. Hammer, Hugo & Tjelmeland, Håkon, 2011. "Approximate forward-backward algorithm for a switching linear Gaussian model," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 154-167, January.
  63. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.
  64. Matthew Klepacz, 2021. "Price Setting and Volatility: Evidence from Oil Price Volatility Shocks," International Finance Discussion Papers 1316, Board of Governors of the Federal Reserve System (U.S.).
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