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Citations for "Efficient High-Dimensional Importance Sampling"

by Jean-Francois Richard

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  1. McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 2008-03, Universite de Montreal, Departement de sciences economiques.
  2. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2012. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," Tinbergen Institute Discussion Papers 12-020/4, Tinbergen Institute.
  3. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007,11, Christian-Albrechts-University of Kiel, Department of Economics.
  4. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.
  5. Tore Selland Kleppe & Hans J. Skaug & Jun Yu, 2009. "Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models," Working Papers CoFie-09-2009, Sim Kee Boon Institute for Financial Economics.
  6. Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers 481, Bank of England.
  7. Blazsek, Szabolcs & Escribano, Alvaro, 2010. "Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors," Journal of Econometrics, Elsevier, vol. 159(1), pages 14-32, November.
  8. Kleppe, Tore Selland & Skaug, Hans Julius, 2012. "Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3105-3119.
  9. Hautsch, Nikolaus & Ou, Yangguoyi, 2009. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," CFS Working Paper Series 2009/03, Center for Financial Studies (CFS).
  10. Siem Jan Koopman & Rutger Lit & Thuy Minh Nguyen, 2012. "Fast Efficient Importance Sampling by State Space Methods," Tinbergen Institute Discussion Papers 12-008/4, Tinbergen Institute, revised 16 Oct 2014.
  11. 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.
  12. Roman Liesenfeld & Guilherme Valle Moura & Jean-François Richard, 2010. "Determinants and Dynamics of Current Account Reversals: An Empirical Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 486-517, 08.
  13. Siem Jan Koopman & Rutger Lit & Andr� Lucas, 2014. "The Dynamic Skellam Model with Applications," Tinbergen Institute Discussion Papers 14-032/IV/DSF73, Tinbergen Institute.
  14. Kleppe, Tore Selland & Yu, Jun & Skaug, Hans J., 2014. "Maximum likelihood estimation of partially observed diffusion models," Journal of Econometrics, Elsevier, vol. 180(1), pages 73-80.
  15. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
  16. Liesenfeld, Roman & Richard, Jean-François, 2006. "Improving MCMC Using Efficient Importance Sampling," Economics Working Papers 2006,05, Christian-Albrechts-University of Kiel, Department of Economics.
  17. BAUWENS, Luc & GALLI, Fausto, . "Efficient importance sampling for ML estimation of SCD models," CORE Discussion Papers RP -2088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  18. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
  19. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2009. "Efficient Likelihood Evaluation of State-Space Representations," Working Papers 2009/15, Czech National Bank, Research Department.
  20. Szabolcs Blazsek & Álvaro Escribano, 2014. "Propensity to patent, R&D and market competition : dynamic spillovers of innovation leaders and followers," Economics Working Papers we1412, Universidad Carlos III, Departamento de Economía.
  21. Jung, Robert C. & Liesenfeld, Roman & Richard, Jean-François, 2011. "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 73-85.
  22. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," CORE Discussion Papers 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  23. Kleppe, Tore Selland & Skaug, Hans J., 2008. "Simulated maximum likelihood for general stochastic volatility models: a change of variable approach," MPRA Paper 12022, University Library of Munich, Germany.
  24. Tsyplakov Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," EERC Working Paper Series 10/09e, EERC Research Network, Russia and CIS.
  25. Tommaso, Proietti & Alessandra, Luati, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," MPRA Paper 39600, University Library of Munich, Germany.
  26. Christian N. Brinch, 2008. "Simulated Maximum Likelihood using Tilted Importance Sampling," Discussion Papers 540, Statistics Norway, Research Department.
  27. Istv�n Barra & Lennart Hoogerheide & Siem Jan Koopman & Andr� Lucas, 2014. "Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers 14-118/III, Tinbergen Institute.
  28. Wu, Xin-Yu & Ma, Chao-Qun & Wang, Shou-Yang, 2012. "Warrant pricing under GARCH diffusion model," Economic Modelling, Elsevier, vol. 29(6), pages 2237-2244.
  29. Liesenfeld, Roman & Richard, Jean-François, 2010. "Efficient estimation of probit models with correlated errors," Journal of Econometrics, Elsevier, vol. 156(2), pages 367-376, June.
  30. Vêlayoudom Marimoutou & Manel Soury, 2015. "Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model," AMSE Working Papers 1520, Aix-Marseille School of Economics, Marseille, France.
  31. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
  32. Moura, Guilherme V. & Turatti, Douglas Eduardo, 2014. "Efficient estimation of conditionally linear and Gaussian state space models," Economics Letters, Elsevier, vol. 124(3), pages 494-499.
  33. Christian Brinch, 2012. "Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling," Computational Statistics, Springer, vol. 27(1), pages 13-28, March.
  34. Hafner Christian M. & Manner Hans, 2008. "Dynamic stochastic copula models: Estimation, inference and applications," Research Memorandum 043, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  35. Hautsch, Nikolaus, 2007. "Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model," CFS Working Paper Series 2007/25, Center for Financial Studies (CFS).
  36. Alvaro Escribano & Szabolcs Blazsek, 2012. "Patents, secret innovations and firm's rate of return : differential effects of the innovation leader," Economics Working Papers we1202, Universidad Carlos III, Departamento de Economía.
  37. Jun Yu, 2008. "A Semiparametric Stochastic Volatility Model," Working Papers CoFie-04-2008, Sim Kee Boon Institute for Financial Economics.
  38. Tore Selland Kleppe & Jun Yu & Hans J. skaug, 2011. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 10-2011, Singapore Management University, School of Economics.
  39. Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.
  40. Andre A. Monteiro, 2009. "The econometrics of randomly spaced financial data: a survey," Statistics and Econometrics Working Papers ws097924, Universidad Carlos III, Departamento de Estadística y Econometría.
  41. Bretó, Carles, 2014. "On idiosyncratic stochasticity of financial leverage effects," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 20-26.
  42. Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2011. "Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers 11-057/4, Tinbergen Institute, revised 27 Jan 2012.
  43. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
  44. Istv�n Barra & Lennart Hoogerheide & Siem Jan Koopman & Andr� Lucas, 2014. "Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models," Tinbergen Institute Discussion Papers 14-118/III, Tinbergen Institute.
  45. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.
  46. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, vol. 149(1), pages 2-11, April.
  47. Xiuping Mao & Esther Ruiz & Helena Veiga, 2014. "Score driven asymmetric stochastic volatility models," Statistics and Econometrics Working Papers ws142618, Universidad Carlos III, Departamento de Estadística y Econometría.
  48. Siem Jan Koopman & Rutger Lit & Andr� Lucas, 2014. "The Dynamic Skellam Model with Applications," Tinbergen Institute Discussion Papers 14-032/IV/DSF73, Tinbergen Institute.
  49. Jun Yu & Renate Meyer, 2004. "Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison," Working Papers 23-2004, Singapore Management University, School of Economics.
  50. Charles S. Bos, 2011. "Relating Stochastic Volatility Estimation Methods," Tinbergen Institute Discussion Papers 11-049/4, Tinbergen Institute.
  51. Steffen Henzel & Malte Rengel, 2014. "Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis," CESifo Working Paper Series 4991, CESifo Group Munich.
  52. Kleppe, Tore Selland & Liesenfeld, Roman, 2011. "Efficient high-dimensional importance sampling in mixture frameworks," Economics Working Papers 2011,11, Christian-Albrechts-University of Kiel, Department of Economics.
  53. Sun, Libo & Lee, Chihoon & Hoeting, Jennifer A., 2015. "A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 54-67.
  54. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
  55. Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2753-2762, November.
  56. Tsyplakov, Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," MPRA Paper 26908, University Library of Munich, Germany.
  57. Andreasen, Martin M., 2011. "Non-linear DSGE models and the optimized central difference particle filter," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1671-1695, October.
  58. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Papers 367, University of Pittsburgh, Department of Economics, revised Sep 2008.
  59. Andreas Ziegler, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 230(5), pages 630-652, October.
  60. Liesenfeld, Roman & Richard, Jean-François, 2010. "The dynamic invariant multinomial probit model: Identification, pretesting and estimation," Journal of Econometrics, Elsevier, vol. 155(2), pages 117-127, April.
  61. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
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