Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/301118 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
LEM Working Paper Series No. 2024/18
Verlag: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Zusammenfassung: 
Linking the statistic and the machine learning literature, we provide new general results on the convergence of stochastic approximation schemes and inexact Newton methods. Building on these results, we put forward a new optimization scheme that we call generalized inexact Newton method (GINM). We extensively discuss the theoretical and the computational aspects of the GINM. The results apply to both deterministic and stochastic approximation schemes, and are particular effective in the case in which the objective function to be optimized is highly irregular and/or the stochastic equicontinuity hypothesis is violated. Examples are common in dynamic discrete choice models and complex simulation models characterized by nonlinearities and high levels of heterogeneity. The theory is supported by extensive Monte Carlo experiments.
Schlagwörter: 
Optimization
stochastic approximation
Newton-Raphson methods
asymptotic convergence
M-estimation
stochastic equicontinuity
JEL: 
C61
C15
C44
Dokumentart: 
Working Paper

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