Maximum approximated likelihood estimation
Maximum approximated likelihood estimation
Dokument öffnen (335.2KB)Datum der Veröffentlichung
08.2019Verlag / herausgebende Einrichtung
Institut für Numerische Simulation (INS)Verlagsort
BonnMetadaten
Zur Langanzeige
Zitierbarer Link (Handle URI) 
https://hdl.handle.net/20.500.11811/11802
Inhalt
Empirical economic research frequently applies maximum likelihood estimation in cases where the likelihood function is analytically intractable. Most of the theoretical literature focuses on maximum simulated likelihood (MSL) estimators, while empirical and simulation analyzes often find that alternative approximation methods such as quasi–Monte Carlo simulation, Gaussian quadrature, and integration on sparse grids behave considerably better numerically. This paper generalizes the theoretical results widely known for MSL estimators to a general set of maximum approximated likelihood (MAL) estimators. We provide general conditions for both the model and the approximation approach to ensure consistency and asymptotic normality. We also show specific examples and finite–sample simulation results.
Klassifikation (DDC)
510 Mathematik 518 Numerische AnalysisErstpublikation
https://ins.uni-bonn.de/publication/preprintsZugehörige Publikation(en)
https://doi.org/10.48550/arXiv.1908.04110Reihe, Nummer
INS Preprints ; 1905Griebel, Michael; Heiss, Florian; Oettershagen, Jens; Weiser, Constantin: Maximum approximated likelihood estimation. In: INS Preprints, 1905.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/11802
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/11802
@unpublished{handle:20.500.11811/11802,
author = {{Michael Griebel} and {Florian Heiss} and {Jens Oettershagen} and {Constantin Weiser}},
title = {Maximum approximated likelihood estimation},
publisher = {Institut für Numerische Simulation (INS)},
year = 2019,
month = aug,
INS Preprints},
volume = 1905,
note = {Empirical economic research frequently applies maximum likelihood estimation in cases where the likelihood function is analytically intractable. Most of the theoretical literature focuses on maximum simulated likelihood (MSL) estimators, while empirical and simulation analyzes often find that alternative approximation methods such as quasi–Monte Carlo simulation, Gaussian quadrature, and integration on sparse grids behave considerably better numerically. This paper generalizes the theoretical results widely known for MSL estimators to a general set of maximum approximated likelihood (MAL) estimators. We provide general conditions for both the model and the approximation approach to ensure consistency and asymptotic normality. We also show specific examples and finite–sample simulation results.},
url = {https://hdl.handle.net/20.500.11811/11802}
}
author = {{Michael Griebel} and {Florian Heiss} and {Jens Oettershagen} and {Constantin Weiser}},
title = {Maximum approximated likelihood estimation},
publisher = {Institut für Numerische Simulation (INS)},
year = 2019,
month = aug,
INS Preprints},
volume = 1905,
note = {Empirical economic research frequently applies maximum likelihood estimation in cases where the likelihood function is analytically intractable. Most of the theoretical literature focuses on maximum simulated likelihood (MSL) estimators, while empirical and simulation analyzes often find that alternative approximation methods such as quasi–Monte Carlo simulation, Gaussian quadrature, and integration on sparse grids behave considerably better numerically. This paper generalizes the theoretical results widely known for MSL estimators to a general set of maximum approximated likelihood (MAL) estimators. We provide general conditions for both the model and the approximation approach to ensure consistency and asymptotic normality. We also show specific examples and finite–sample simulation results.},
url = {https://hdl.handle.net/20.500.11811/11802}
}
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