Prediction-based estimating functions

By Michael SØrensen. 2000

133 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7f5c84e4aaea5fbeb4a238dedbc8b140c49206c5

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Citations in the corpus

A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “Genon-Catalot, Jeantheau, and Lar edo (2000) and Srensen (2000) .”
Parameter Estimation for a Discretely Observed Integrated Diffusion Process (2006, background): “Under the assumption of fixed sampling interval, different kinds of estimating functions have been studied (see e.g. Barndorff-Nielsen & Sørensen, 1994; Kessler & Sørensen, 1999; Sørensen, 1999, 2000; Kessler, 2000; Bibby & Sørensen, 2001).”
Estimation for discretely observed diffusions using transform functions (2003, background): “Sørensen (2000) considered more general estimating functions, known as prediction-based estimating functions, where conditional moments are approximated by expressions involving only unconditional moments.”
Econometric analysis of realized volatility and its use in estimating stochastic volatility models (2002, background): “A very incomplete list of references includes Gourieroux et al. (1993), Gallant and Long (1997), Kim et al. (1998), Elerian et al. (2001) and Sørensen (2000).”
The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes (2008, unknown): “For the diffusions with a symmetric /-distribution or an inverse Gamma distribution, Wong (1964) gave a spectral expression of the transition density with two parts: a finite sum similar to (11) and an integral over the continuous spectrum involving eigenfunctions corresponding to this part of the s”
CHAPTER 4 – Estimating Functions for Discretely Sampled Diffusion-Type Models (2010, methodology): “For the stochastic volatility model (3.26), to ensure (1) in Condition 3.7 it is enough that the volatility process v is geometrically α-mixing, see Lemma 6.3 in Sørensen (2000) or Genon-Catalot, Jeantheau & Larédo (2000).”
Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations (2006, background): “Sørensen (2000) considers prediction based estimating functions which involve approximating conditional moments with expressions based on unconditional moments.”
Comment: A Selective Overview of Nonparametric Methods in Financial Econometrics (2005, methodology): “When the data are non-Markovian, it is usually not possible to find a tractable martingale estimating function, but an alternative is provided by the prediction-based estimating functions proposed in [ 21 ], which can be interpreted as approximations to martingale estimating functions.”