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Causal Analysis with Event History Data Using Stata

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.07.2025

Abbildungen

62 SW-Abb., 62 SW-Zeichn.

Verlag

Taylor & Francis

Seitenzahl

248

Maße (L/B/H)

29,7/21/1,4 cm

Gewicht

654 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-1-03-265778-3

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.07.2025

Abbildungen

62 SW-Abb., 62 SW-Zeichn.

Verlag

Taylor & Francis

Seitenzahl

248

Maße (L/B/H)

29,7/21/1,4 cm

Gewicht

654 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-1-03-265778-3

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Taylor & Francis Verlag GmbH
Kaufingerstraße 24
80331 München
DE
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  • Produktbild: Causal Analysis with Event History Data Using Stata

  • Start v
    Preface

    1 Introduction

    1.1 Causal Modeling and Observation Plans
    1.1.1 Cross-Sectional Data
    1.1.2 Panel Data
    1.1.3 Event History Data
    1.2 Event History Analysis and Causal Modeling
    1.2.1 Causal Explanations
    1.2.2 Transition Rate Models
    2 Event History Data Structures
    2.1 Basic Terminology
    2.2 Event History Data Organization
    3 Nonparametric Descriptive Methods
    3.1 Life Table Method
    3.2 Product-Limit Estimation
    3.3 Comparing Survivor Functions
    4 Exponential Transition Rate Models
    4.1 The Basic Exponential Model
    4.1.1 Maximum Likelihood Estimation
    4.1.2 Models without Covariates
    4.1.3 Time-Constant Covariates
    4.2 Models with Multiple Destinations
    4.3 Models with Multiple Episodes
    5 Piecewise Constant Exponential Models
    5.1 The Basic Model
    5.2 Models without Covariates
    5.3 Models with Proportional Covariate Effects
    5.4 Models with Period-Specific Effects
    6 Exponential Models with Time-Dependent Covariates
    6.1 Parallel and Interdependent Processes
    6.2 Interdependent Processes: The System Approach
    6.3 Interdependent Processes: The Causal Approach
    6.4 Episode Splitting with Qualitative Covariates
    6.5 Episode Splitting with Quantitative Covariates
    6.6 Application Examples
    7 Parametric Models of Time Dependence
    7.1 Interpretation of Time Dependence
    7.2 Gompertz Models
    7.3 Weibull Models
    7.4 Log-Logistic Models
    7.5 Log-Normal Models
    8 Methods for Testing Parametric Assumptions
    8.1 Simple Graphical Methods
    8.2 Pseudoresiduals
    9 Semiparametric Transition Rate Models
    9.1 Partial Likelihood Estimation
    9.2 Time-Dependent Covariates
    9.3 The Proportionality Assumption
    9.4 Stratification with Covariates and for Multiepisode Data
    9.5 Baseline Rates and Survivor Functions
    9.6 Application Example
    10 Problems of Model Specification
    10.1 Unobserved Heterogeneity
    10.2 Models with a Mixture Distribution
    10.2.1 Models with a Gamma Mixture
    10.2.2 Exponential Models with a Gamma Mixture
    10.2.3 Weibull Models with a Gamma Mixture
    10.2.4 Random Effects for Multiepisode Data
    10.3 Discussion
    11 Sequence Analysis
    Brendan Halpin
    11.1 What is Sequence Analysis?
    11.2 Defining Distances
    11.3 Doing Sequence Analysis in Stata
    11.4 Unary Summaries
    11.5 Intersequence Distance
    11.6 What to Do with Sequence Distances?
    11.7 Optimal Matching Distance
    11.8 Special Topics
    11.9 Conclusion
    Appendix: Exercises
    References
    About the Authors