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

• Autorzy: Schrey Christopher , Schäffer Tobias , Militzer-Horstmann Carsta , Kossack Nils

Identyfikatory

DOI

10.15439/2019F354

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

endogeneity; likelihood estimation; likelihood

PL

endogeniczność; szacowanie prawdopodobieństwa; wiarygodność

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 19
• Strony: 79--82
• Opis fizyczny: Bibliogr. 22 poz., wz., il.

Twórcy

autor

Schrey Christopher

• WIG2 Institute, Leipzig
• Leipzig University

autor

Schäffer Tobias

• WIG2 Institute, Leipzig
• Martin Luther University of Halle-Wittenberg

autor

Militzer-Horstmann Carsta

• WIG2 Institute, Leipzig
• Leipzig University

autor

Kossack Nils

• WIG2 Institute, Leipzig

Bibliografia

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Uwagi

1. Track 4: Information Systems and Technologies

2. Technical Session: 1st Special Session on Data Science in Health

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).