Abstract
Simulation-based estimation is an application of the general Monte Carlo principle to statistical estimation: any mathematical expectation, when unavailable in closed form, can be approximated to any desired level of accuracy through a generation of (pseudo-) random numbers. Pseudo-random numbers are generated on a computer by means of a deterministic method. (For convenience, we henceforth delete the qualification ‘pseudo’.) Then a well-suited drawing of random numbers (or vectors) Z 1 , Z 2 ,…,Z H provides the Monte Carlo simulator(1=H)Σ h=1g(Zh) H of E[g(Z)]. Of course, one may also want to resort to many simulators improving upon this naive one in terms of variance reduction, increased smoothness and reduced computational cost. A detailed discussion of simulation techniques is beyond the scope of this article. Nor are we going to study Monte Carlo experiments, which complement a given statistical procedure by the observation of its properties on simulated data. Rather, our focus of interest is to show how Monte Carlo integration may directly help to compute estimators that would be unfeasible without resorting to simulators.
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Renault, E. (2010). Simulation-Based Estimation. In: Durlauf, S.N., Blume, L.E. (eds) Microeconometrics. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280816_31
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DOI: https://doi.org/10.1057/9780230280816_31
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