Fragility of identification in panel binary response models

2019 ◽  
Vol 22 (3) ◽  
pp. 282-291
Author(s):  
Giovanni Forchini ◽  
Bin Jiang
Keyword(s):  
Simple Model ◽  
Random Effects ◽  
Binary Response ◽  
Response Model ◽  
Exogenous Variables ◽  
Response Models ◽  
Joint Distributions ◽  
Binary Response Model ◽  
Binary Response Models ◽  
Over Time

Summary The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible—including in the logistic case—to find a distribution of the random effects given the exogenous variables, such that the slopes' parameters are arbitrarily different, but the joint distributions of the binary response variables are arbitrarily close.

scholarly journals Binary Response Models: Logits, Probits and Semiparametrics

2001 ◽  
Vol 15 (4) ◽  
pp. 43-56 ◽  
Author(s):  
Joel L Horowitz ◽  
N.E Savin
Keyword(s):  
Probability Model ◽  
Binary Response ◽  
Linear Probability Model ◽  
Response Models ◽  
Explanatory Variables ◽  
Nonparametric Models ◽  
Binary Response Model ◽  
Unrealistic Assumption ◽  
Binary Response Models ◽  
Linear Probability

A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.

scholarly journals Conjoint Analysis of Groundwater Protection Programs

1997 ◽  
Vol 26 (2) ◽  
pp. 229-236 ◽  
Author(s):  
Thomas H. Stevens ◽  
Christopher Barrett ◽  
Cleve E. Willis
Keyword(s):  
Contingent Valuation ◽  
Conjoint Analysis ◽  
Groundwater Protection ◽  
Economic Value ◽  
Model Specification ◽  
Binary Response ◽  
Response Model ◽  
Dichotomous Choice ◽  
Binary Response Model

Three conjoint models—a traditional ratings model, a ratings difference specification, and a binary response model—were used to value groundwater protection program alternatives. The last, which is virtually identical to a dichotomous choice contingent valuation specification, produced the smallest value estimates. This suggests that the conjoint model is very sensitive to model specification and that traditional conjoint models may overestimate economic value because many respondents are not in the market for the commodity being valued.

scholarly journals Binary response models withM-phase case-control data

2013 ◽  
Vol 116 ◽  
pp. 332-348
Author(s):  
Chin-Tsang Chiang ◽  
Ming-Yueh Huang ◽  
Ren-Hong Bai
Keyword(s):  
Case Control ◽  
Binary Response ◽  
Control Data ◽  
Response Models ◽  
Binary Response Models
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