Techniques of Event History Modeling: New

Including new developments and publications which have appeared since the publication of the first edition in 1995, this second edition: *gives a comprehensive introductory account of event history modeling techniques and their use in applied research in economics and the social sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history models employ the time-path of changes in states and relate changes in causal variables in the past to changes in discrete outcomes in the future; and *introduces the reader to the computer program Transition Data Analysis (TDA). This software estimates the sort of models most frequently used with longitudinal data, in particular, discrete-time and continuous-time event history data. Techniques of Event History Modeling can serve as a student textbook in the fields of statistics, economics, the social sciences, psychology, and the political sciences. It can also be used as a reference for scientists in all fields of research.

Preface	p. vii
Introduction	p. 1
Causal Modeling and Observation Plans	p. 4
Cross-Sectional Data	p. 5
Panel Data	p. 13
Event History Data	p. 19
Event History Analysis and Causal Modeling	p. 21
Event History Data Structures	p. 38
Basic terminology	p. 38
Event History Data Organization	p. 42
Nonparametric Descriptive Methods	p. 56
Life Table Method	p. 56
Product-Limit Estimation	p. 71
Comparing Survivor Functions	p. 76
Exponential Transition Rate Models	p. 86
The Basic Exponential Model	p. 87
Maximum Likelihood Estimation	p. 88
Models without Covariates	p. 91
Time-Constant Covariates	p. 95
Models with Multiple Destinations	p. 101
Models with Multiple Episodes	p. 111
Piecewise Constant Exponential Models	p. 120
The Basic Model	p. 120
Models without Covariates	p. 122
Models with Proportional Covariate Effects	p. 125
Models with Period-Specific Effects	p. 125
Exponential Models with Time-Dependent Covariates	p. 131
Parallel and Interdependent Processes	p. 131
Interdependent Processes: The System Approach	p. 134
Interdependent Processes: The Causal Approach	p. 138
Episode Splitting with Qualitative Covariates	p. 140
Episode Splitting with Quantitative Covariates	p. 152
Application Examples	p. 157
Parametric Models of Time-Dependence	p. 176
Interpretation of Time-Dependence	p. 177
Gompertz-Makeham Models	p. 180
Weibull Models	p. 191
Log-Logistic Models	p. 197
Log-Normal Models	p. 202
Sickle Models	p. 208
Methods to Check Parametric Assumptions	p. 213
Simple Graphical Methods	p. 213
Pseudoresiduals	p. 219
Semi-Parametric Transition Rate Models	p. 228
Partial Likelihood Estimation	p. 229
Time-Dependent Covariates	p. 234
The Proportionality Assumption	p. 240
Baseline Rates and Survivor Functions	p. 248
Application Example	p. 251
Problems of Model Specification	p. 255
Unobserved Heterogeneity	p. 255
Models with a Mixture Distribution	p. 261
Models with a Gamma Mixture	p. 264
Exponential Models with a Gamma Mixture	p. 267
Weibull Models with a Gamma Mixture	p. 269
Discussion	p. 274
Basic Information About TDA	p. 279
References	p. 282
About the Authors	p. 304
Index	p. 305
Table of Contents provided by Syndetics. All Rights Reserved.

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Techniques of Event History Modeling: New Approaches to Casual Analysis

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