
Econometric Analysis of Count Data
Hardcover | 8 April 2008 | Edition Number 5
At a Glance
352 Pages
Revised
16.3 x 24.1 x 2.9
Hardcover
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Industry Reviews
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"Winkleman has published numerous articles on using content models in economics and other social science journals. Because these are both applied and theoretical, he is well suited to write a monograph in this area. This book provides a very useful survey for anyone doing serious research using count data...for those who are doing substantive research using count data, [this book] will prove quite useful."
From the reviews of the fourth edition:
"The main objective of the book is to introduce count models at a graduate level so that these models can be used by students, researchers or interested practitioners. ... For all researchers who are concerned with count data the book offers a very good introduction into this field of research and many examples and interpretations of the results. Therefore, the book provides an excellent starting point for working in this area of applied research." (Herbert S. Buscher, Zentralblatt MATH, Vol. 1032 (7), 2004)
| Preface | p. V |
| Introduction | p. 1 |
| Poisson Regression Model | p. 1 |
| Examples | p. 2 |
| Organization of the Book | p. 4 |
| Probability Models for Count Data | p. 7 |
| Introduction | p. 7 |
| Poisson Distribution | p. 7 |
| Definitions and Properties | p. 7 |
| Genesis of the Poisson Distribution | p. 10 |
| Poisson Process | p. 11 |
| Generalizations of the Poisson Process | p. 14 |
| Poisson Distribution as a Binomial Limit | p. 15 |
| Exponential Interarrival Times | p. 16 |
| Non-Poissonness | p. 17 |
| Further Distributions for Count Data | p. 20 |
| Negative Binomial Distribution | p. 20 |
| Binomial Distribution | p. 25 |
| Logarithmic Distribution | p. 27 |
| Summary | p. 28 |
| Modified Count Data Distributions | p. 30 |
| Truncation | p. 30 |
| Censoring and Grouping | p. 31 |
| Altered Distributions | p. 32 |
| Generalizations | p. 33 |
| Mixture Distributions | p. 33 |
| Compound Distributions | p. 36 |
| Birth Process Generalizations | p. 39 |
| Katz Family of Distributions | p. 40 |
| Additive Log-Differenced Probability Models | p. 41 |
| Linear Exponential Families | p. 42 |
| Summary | p. 44 |
| Distributions for Over- and Underdispersion | p. 45 |
| Generalized Event Count Model | p. 45 |
| Generalized Poisson Distribution | p. 46 |
| Poisson Polynomial Distribution | p. 47 |
| Double Poisson Distribution | p. 49 |
| Summary | p. 49 |
| Duration Analysis and Count Data | p. 50 |
| Distributions for Interarrival Times | p. 52 |
| Renewal Processes | p. 54 |
| Gamma Count Distribution | p. 56 |
| Duration Mixture Models | p. 59 |
| Poisson Regression | p. 63 |
| Specification | p. 63 |
| Introduction | p. 63 |
| Assumptions of the Poisson Regression Model | p. 63 |
| Ordinary Least Squares and Other Alternatives | p. 65 |
| Interpretation of Parameters | p. 70 |
| Period at Risk | p. 74 |
| Maximum Likelihood Estimation | p. 77 |
| Introduction | p. 77 |
| Likelihood Function and Maximization | p. 77 |
| Newton-Raphson Algorithm | p. 78 |
| Properties of the Maximum Likelihood Estimator | p. 80 |
| Estimation of the Variance Matrix | p. 82 |
| Approximate Distribution of the Poisson Regression Coefficients | p. 83 |
| Bias Reduction Techniques | p. 84 |
| Pseudo-Maximum Likelihood | p. 87 |
| Linear Exponential Families | p. 89 |
| Biased Poisson Maximum Likelihood Inference | p. 90 |
| Robust Poisson Regression | p. 91 |
| Non-Parametric Variance Estimation | p. 95 |
| Poisson Regression and Log-Linear Models | p. 97 |
| Generalized Method of Moments | p. 98 |
| Sources of Misspecification | p. 102 |
| Mean Function | p. 102 |
| Unobserved Heterogeneity | p. 103 |
| Measurement Error | p. 105 |
| Dependent Process | p. 107 |
| Selectivity | p. 107 |
| Simultaneity and Endogeneity | p. 108 |
| Underreporting | p. 109 |
| Excess Zeros | p. 109 |
| Variance Function | p. 110 |
| Testing for Misspecification | p. 112 |
| Classical Specification Tests | p. 112 |
| Regression Based Tests | p. 118 |
| Goodness-of-Fit Tests | p. 118 |
| Tests for Non Nested Models | p. 120 |
| Outlook | p. 125 |
| Unobserved Heterogeneity | p. 127 |
| Introduction | p. 127 |
| Conditional Mean Function | p. 127 |
| Partial Effects with Unobserved Heterogeneity | p. 128 |
| Unobserved Heterogeneity in the Poisson Model | p. 129 |
| Parametric and Semi-Parametric Models | p. 130 |
| Parametric Mixture Models | p. 130 |
| Gamma Mixture | p. 131 |
| Inverse Gaussian Mixture | p. 131 |
| Log-Normal Mixture | p. 132 |
| Negative Binomial Models | p. 134 |
| Negbin II Model | p. 135 |
| Negbin I Model | p. 136 |
| Negbink Model | p. 136 |
| NegbinX Model | p. 137 |
| Semiparametric Mixture Models | p. 138 |
| Series Expansions | p. 138 |
| Finite Mixture Models | p. 139 |
| Sample Selection and Endogeneity | p. 143 |
| Censoring and Truncation | p. 143 |
| Truncated Count Data Models | p. 144 |
| Endogenous Sampling | p. 144 |
| Censored Count Data Models | p. 146 |
| Grouped Poisson Regression Model | p. 147 |
| Incidental Censoring and Truncation | p. 148 |
| Outcome and Selection Model | p. 148 |
| Models of Non-Random Selection | p. 149 |
| Bivariate Normal Error Distribution | p. 150 |
| Outcome Distribution | p. 152 |
| Incidental Censoring | p. 153 |
| Incidental Truncation | p. 154 |
| Endogeneity in Count Data Models | p. 156 |
| Introduction and Examples | p. 156 |
| Parameter Ancillarity | p. 157 |
| Endogeneity and Mean Function | p. 159 |
| A Two-Equation Framework | p. 161 |
| Instrumental Variable Estimation | p. 162 |
| Estimation in Stages | p. 165 |
| Switching Regression | p. 167 |
| Full Information Maximum Likelihood Estimation | p. 168 |
| Moment-Based Estimation | p. 170 |
| Non-Normality | p. 171 |
| Mixed Discrete-Continuous Models | p. 171 |
| Zeros in Count Data Models | p. 173 |
| Introduction | p. 173 |
| Zeros in the Poisson Model | p. 174 |
| Excess Zeros and Overdispersion | p. 174 |
| Two-Crossings Theorem | p. 175 |
| Effects at the Extensive Margin | p. 176 |
| Multi-Index Models | p. 177 |
| A General Decomposition Result | p. 177 |
| Hurdle Count Data Models | p. 178 |
| Hurdle Poisson Model | p. 181 |
| Marginal Effects | p. 182 |
| Hurdle Negative Binomial Model | p. 183 |
| Non-nested Hurdle Models | p. 183 |
| Unobserved Heterogeneity in Hurdle Models | p. 185 |
| Finite Mixture Versus Hurdle Models | p. 186 |
| Correlated Hurdle Models | p. 187 |
| Zero-Inflated Count Data Models | p. 188 |
| Introduction | p. 188 |
| Zero-Inflated Poisson Model | p. 189 |
| Zero-Inflated Negative Binomial Model | p. 191 |
| Marginal Effets | p. 191 |
| Compound Count Data Models | p. 192 |
| Multi-Episode Models | p. 193 |
| Underreporting | p. 193 |
| Count Amount Model | p. 196 |
| Endogenous Underreporting | p. 197 |
| Quantile Regression for Count Data | p. 199 |
| Correlated Count Data | p. 203 |
| Multivariate Count Data | p. 203 |
| Multivariate Poisson Distribution | p. 205 |
| Multivariate Negative Binomial Model | p. 210 |
| Multivariate Poisson-Gamma Mixture Model | p. 212 |
| Multivariate Poisson-Log-Normal Model | p. 213 |
| Latent Poisson-Normal Model | p. 216 |
| Moment-Based Methods | p. 217 |
| Copula Functions | p. 219 |
| Panel Data Models | p. 220 |
| Fixed Effects Poisson Model | p. 222 |
| Moment-based Estimation of the Fixed Effects Model | p. 225 |
| Fixed Effects Negative Binomial Model | p. 227 |
| Random Effects Count Data Models | p. 228 |
| Dynamic Panel Count Data Models | p. 230 |
| Time-Series Count Data Models | p. 232 |
| Bayesian Analysis of Count Data | p. 241 |
| Bayesian Analysis of the Poisson Model | p. 242 |
| A Poisson Model with Underreporting | p. 245 |
| Estimation of the Multivariate Poisson-Log-Normal Model by MCMC | p. 247 |
| Estimation of a Random Coefficients Model by MCMC | p. 248 |
| Applications | p. 251 |
| Accidents | p. 251 |
| Crime | p. 252 |
| Trip Frequency | p. 252 |
| Health Economics | p. 254 |
| Demography | p. 257 |
| Marketing and Management | p. 260 |
| Labor Mobility | p. 261 |
| Economics Models of Labor Mobility | p. 262 |
| Previous Literature | p. 263 |
| Data and Descriptive Statistics | p. 265 |
| Regression Results | p. 269 |
| Model Performance | p. 272 |
| Marginal Probability Effects | p. 274 |
| Structural Inferences | p. 278 |
| Probability Generating Functions | p. 281 |
| Gauss-Hermite Quadrature | p. 285 |
| Software | p. 289 |
| Tables | p. 291 |
| References | p. 299 |
| Author's Index | p. 321 |
| Subject Index | p. 327 |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9783540776482
ISBN-10: 3540776486
Published: 8th April 2008
Format: Hardcover
Language: English
Number of Pages: 352
Audience: College, Tertiary and University
Publisher: Springer Nature B.V.
Country of Publication: GB
Edition Number: 5
Edition Type: Revised
Dimensions (cm): 16.3 x 24.1 x 2.9
Weight (kg): 0.67
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