Identification of Binary Response Models PDF Download
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Author: Charles F. Manski
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Author: Charles F. Manski
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Author: Shakeeb Khan
Publisher:
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Category :
Languages : en
Pages :
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Author: Michel Mouchart
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Author: Kenneth Young Chay
Publisher:
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Category : Economic history
Languages : en
Pages : 64
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Author: Andrew Chesher
Publisher:
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Category :
Languages : en
Pages :
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The paper studies the partial identifying power of structural single equation threshold crossing models for binary responses when explanatory variables may be endogenous. The paper derives the sharp identified set of threshold functions for the case in which explanatory variables are discrete and provides a constructive proof of sharpness. There is special attention to a widely employed semiparametric shape restriction which requires the threshold crossing function to be a monotone function of a linear index involving the observable explanatory variables. It is shown that the restriction brings great computational benefits, allowing direct calculation of the identified set of index coefficients without calculating the nonparametrically specified threshold function. With the restriction in place the methods of the paper can be applied to produce identified sets in a class of binary response models with mis-measured explanatory variables. -- Binary Response ; Endogeneity ; Incomplete models ; Index Restrictions ; Instrumental variables ; Measurement Error Models ; Partial Identification ; Probit Models ; Shape Restrictions ; Threshold Crossing Models
Author: Stefan Hoderlein
Publisher:
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Category :
Languages : en
Pages :
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Author: Kenneth Y. Chay
Publisher:
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Category :
Languages : en
Pages : 0
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We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation (SIPP). These include alternative random effects models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypotheses that the sample initial conditions are either exogenous or in equilibrium are rejected by the data. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The fixed effects conditional legit estimates are similar to the estimates from the random effects model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. Heterogeneity appears to explain about 50% and 70% of the overall persistence in welfare and labor force participation, respectively. In addition, for female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.
Author: Gerhard Tutz
Publisher: Cambridge University Press
ISBN: 1139499580
Category : Mathematics
Languages : en
Pages : 573
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This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.
Author: D.R. Cox
Publisher: Routledge
ISBN: 1351466739
Category : Mathematics
Languages : en
Pages : 240
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The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods. There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.
Author: William A. Brock
Publisher:
ISBN:
Category :
Languages : en
Pages : 59
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