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2 Annals of Computer Science and Information Systems

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Maximum simulated likelihood: don't stop believin'?

Schrey Christopher

Annals of Computer Science and Information Systems

2021

Vol. 26

175--180

Unobserved heterogeneity may complicate model estimation in econometrics. To integrate out the effect of unobserved heterogeneity via maximum simulated likelihood (MSL) estimation, assumptions regarding the underlying distribution need to be made. Researchers seldomly discuss these assumptions. This raises the question, to what extent estimation results in the MSL-context are robust to potential distributional mismatch. This work-in-progress derives the research question from the literature. A simulation study is conducted that underpins the relevance of this matter, where results imply that mismatch may introduce significant bias. Intended future work to properly address and answer this question is defined and discussed.

Maximum simulated likelihood: Don’t stop ’til you get enough?

Schrey Christopher, Schäffer Tobias, Militzer-Horstmann Carsta, Kossack Nils

Annals of Computer Science and Information Systems

2019

Vol. 19

79--82

Maximum simulated likelihood estimation can be employed in empirical health economics, amongst others, to tackle issues concerning endogenous treatment effects. While theory suggests that maximum simulated likelihood estimation is asymptotically consistent, efficient and equivalent to the maximum likelihood estimator when both the number of simulation draws S and sample size N → ∞ and √N/S → 0 there is no guidance on how large of an S to choose and even theory suggests to experiment. This piece of research reviews strategies of health economists that aim at dealing with this issue. Most pieces of applied research rely on experimentation until numerical stability is achieved, while some employ Monte-Carlo techniques to justify their choice of S. A more formal test was suggested, but seemed not to be employed yet. This lack of guidance induces a research problem that needs to be properly addressed.