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Simulation-based econometric methods / Christian Gourieroux and Alain Monfort.

By: Contributor(s): Material type: TextTextSeries: CORE lecturesPublication details: Oxford : Oxford University Press, 1996.Description: x, 174 p. : ill. ; 24 cmISBN:
  • 9780198774754
Subject(s): DDC classification:
  • 330.015195 23 G716
Contents:
1. Introduction and Motivations -- 2. The Method of Simulated Moments (MSM) -- 3. Simulated Maximum Likelihood, Pseudo-Maximum Likelihood, and Nonlinear Least Squares Methods -- 4. Indirect Inference -- 5. Applications to Limited Dependent Variable Models -- 6. Applications to Financial Series -- 7. Applications to Switching Regime Models.
Summary: Simulation-Based Econometric Methods introduces a new generation of econometric methods in the classical domain. After linear models leading to analytical expressions for estimators and non-linear models using numerical optimization algorithms, the availability of high-speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented, for instance, by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 330.015195 G716 (Browse shelf(Opens below)) Available 137327
Total holds: 0

Includes bibliographical references and index.

1. Introduction and Motivations --
2. The Method of Simulated Moments (MSM) --
3. Simulated Maximum Likelihood, Pseudo-Maximum Likelihood, and Nonlinear Least Squares Methods --
4. Indirect Inference --
5. Applications to Limited Dependent Variable Models --
6. Applications to Financial Series --
7. Applications to Switching Regime Models.

Simulation-Based Econometric Methods introduces a new generation of econometric methods in the classical domain. After linear models leading to analytical expressions for estimators and non-linear models using numerical optimization algorithms, the availability of high-speed computing has enabled econometricians to consider econometric models without simple analytical expressions. The previous difficulties presented, for instance, by the presence of integrals of large dimensions in the probability density functions or in the moments can be circumvented by a simulation-based approach.

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