Abstract
For a parametric econometric model with possibly latent variables, the simulation tool and Monte Carlo integration provide a versatile minimum distance estimation principle. The general approach is dubbed simulation-based indirect inference. It can take advantage of any instrumental piece of information that identifies the structural parameters. Examples include the simulated method of moments and its simulated-score-matching version. Monte Carlo integration also allows numerical assessment of the criterion to maximize for M-estimation. Asymptotic efficiency is reached by the simulated maximum likelihood or a simulated score technique. Since the simulator is provided by the structural model, the classical trade-off between efficiency and robustness to misspecification must be revisited.
Keywords
- Bias correction
- Bootstrap
- Efficient method of moments
- Extremum estimation
- GARCH models
- Generalized method of moments
- Indirect inference
- Indirect least squares
- Maximum likelihood
- Monte Carlo methods
- Parameter-matching estimators
- Score-matching estimators
- Simulated expectation maximization
- Simulated maximum likelihood
- Simulated method of moments
- Simulated score matching
- Simulation-based estimation
- Simulation-based indirect inference
- Statistical estimation
- Stochastic volatility models
- White noise
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Renault, E. (2018). Simulation-Based Estimation. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2532
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2532
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