Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/241928 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
cemmap working paper No. CWP53/20
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
Conditional distribution functions are important statistical objects for the analysis of a wide class of problems in econometrics and statistics. We propose flexible Gaussian representations for conditional distribution functions and give a concave likelihood formulation for their global estimation. We obtain solutions that satisfy the monotonicity property of conditional distribution functions, including under general misspecification and in finite samples. A Lasso-type penalized version of the corresponding maximum likelihood estimator is given that expands the scope of our estimation analysis to models with sparsity. Inference and estimation results for conditional distribution, quantile and density functions implied by our representations are provided and illustrated with an empirical example and numerical simulations.
Schlagwörter: 
Conditional distribution estimation
conditional quantiles
Gaussian representations
maximum likelihood
misspecification
monotonicity
convex programming
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Dokumentart: 
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