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Estimating Stochastic Differential Equations Using Repeated Eigenfunction Estimation and Neural Networks

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  • Tuncer, Ruhi

    (Galatasaray University Economic Research Center)

Abstract

We propose identifying the drift and the diffusion functions of an ergodic scalar stochastic differential equation using repeated eigenfunction estimation. The transition density will be estimated in a new way involving Kolmogorov’s backward equation, neural networks and functions of our choice. Martingale estimating functions will be used to obtain asymptotic properties.

Suggested Citation

  • Tuncer, Ruhi, 2012. "Estimating Stochastic Differential Equations Using Repeated Eigenfunction Estimation and Neural Networks," GIAM Working Papers 12-5, Galatasaray University Economic Research Center.
    • Handle: RePEc:ris:giamwp:2012_005
    as

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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
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    5. Yacine Ait-Sahalia, 2002. "Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach," Econometrica, Econometric Society, vol. 70(1), pages 223-262, January.
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