scholarly journals New Goodness-of-Fit Diagnostics for Conditional Discrete Response Models

2013 ◽  
Author(s):  
Igor Kheifets ◽  
Carlos Velasco
Keyword(s):  
Goodness Of Fit ◽  
Response Models ◽  
Discrete Response ◽  
Discrete Response Models

scholarly journals New goodness-of-fit diagnostics for conditional discrete response models

2017 ◽  
Vol 200 (1) ◽  
pp. 135-149 ◽  
Author(s):  
Igor Kheifets ◽  
Carlos Velasco
Keyword(s):  
Goodness Of Fit ◽  
Response Models ◽  
Discrete Response ◽  
Discrete Response Models

Missing measurements in discrete response models

1990 ◽  
Vol 32 (3) ◽  
pp. 231-235 ◽  
Author(s):  
Donald Lien ◽  
David Rearden
Keyword(s):  
Missing Measurements ◽  
Response Models ◽  
Discrete Response ◽  
Discrete Response Models

Asymptotic inference in discrete response models

1986 ◽  
Vol 27 (1) ◽  
pp. 179-205 ◽  
Author(s):  
Ludwig Fahrmeir ◽  
Heinz Kaufmann
Keyword(s):  
Asymptotic Inference ◽  
Response Models ◽  
Discrete Response ◽  
Discrete Response Models

scholarly journals Information structure and statistical information in discrete response models

10.3982/qe288 ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 995-1017 ◽  
Author(s):  
Shakeeb Khan ◽  
Denis Nekipelov
Keyword(s):  
Information Structure ◽  
Statistical Information ◽  
Response Models ◽  
Discrete Response ◽  
Discrete Response Models

On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models

1992 ◽  
Vol 8 (4) ◽  
pp. 518-552 ◽  
Author(s):  
Lung-Fei Lee
Keyword(s):  
Likelihood Estimation ◽  
Estimation Methods ◽  
Finite Sample ◽  
Response Models ◽  
Maximum Simulated Likelihood ◽  
Asymptotically Normal ◽  
Simulated Likelihood ◽  
Discrete Response ◽  
Maximum Simulated Likelihood Estimation ◽  
Discrete Response Models

This article considers methods of simulated moments for estimation of discrete response models. It is possible to use the same set of random numbers to simulate the choice probabilities for each individual in the sample. In addition to the method of simulated moments of McFadden, we have considered also maximum simulated likelihood estimation methods. An asymptotic theory for such procedures is provided. The estimators are shown to be consistent and asymptotically normal by the theory of generalizedU-statistics. Asymptotic efficiency is discussed. Monte Carlo experiments on the finite sample performance of the estimators are reported.

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