Econometric analysis of count data / Rainer Winkelmann.

By: Winkelmann, Rainer Material type: Text

TextLanguage: English Publication details: Berlin : Springer, 2008. Edition: 5th edDescription: xiv, 333 p. : ill. ; 25 cmISBN: 9783540776482 (hbk. : alk. paper); 3540776486Subject(s): Econometrics | Time-series analysis | Labor mobility -- Econometric modelsDDC classification: 330 LOC classification: HB139 | .W56 2008Online resources: WorldCat details | E-Book Fulltext

Contents:

TOC Cover -- Preface -- Contents -- 1 Introduction -- 1.1 Poisson Regression Model -- 1.2 Examples -- 1.3 Organization of the Book -- 2 Probability Models for Count Data -- 2.1 Introduction -- 2.2 Poisson Distribution -- 2.2.1 Definitions and Properties -- 2.2.2 Genesis of the Poisson Distribution -- 2.2.3 Poisson Process -- 2.2.4 Generalizations of the Poisson Process -- 2.2.5 Poisson Distribution as a Binomial Limit -- 2.2.6 Exponential Interarrival Times -- 2.2.7 Non-Poissonness -- 2.3 Further Distributions for Count Data -- 2.3.1 Negative Binomial Distribution -- 2.3.2 Binomial Distribution -- 2.3.3 Logarithmic Distribution -- 2.3.4 Summary -- 2.4 Modified Count Data Distributions -- 2.4.1 Truncation -- 2.4.2 Censoring and Grouping -- 2.4.3 Altered Distributions -- 2.5 Generalizations -- 2.5.1 Mixture Distributions -- 2.5.2 Compound Distributions -- 2.5.3 Birth Process Generalizations -- 2.5.4 Katz Family of Distributions -- 2.5.5 Additive Log-Differenced Probability Models -- 2.5.6 Linear Exponential Families -- 2.5.7 Summary -- 2.6 Distributions for Over- and Underdispersion -- 2.6.1 Generalized Event Count Model -- 2.6.2 Generalized Poisson Distribution -- 2.6.3 Poisson Polynomial Distribution -- 2.6.4 Double Poisson Distribution -- 2.6.5 Summary -- 2.7 Duration Analysis and Count Data -- 2.7.1 Distributions for Interarrival Times -- 2.7.2 Renewal Processes -- 2.7.3 Gamma Count Distribution -- 2.7.4 Duration Mixture Models -- 3 Poisson Regression -- 3.1 Specification -- 3.1.1 Introduction -- 3.1.2 Assumptions of the Poisson Regression Model -- 3.1.3 Ordinary Least Squares and Other Alternatives -- 3.1.4 Interpretation of Parameters -- 3.1.5 Period at Risk -- 3.2 Maximum Likelihood Estimation -- 3.2.1 Introduction -- 3.2.2 Likelihood Function and Maximization -- 3.2.3 Newton-Raphson Algorithm -- 3.2.4 Properties of the Maximum Likelihood Estimator -- 3.2.5 Estimation of the Variance Matrix -- 3.2.6 Approximate Distribution of the Poisson Regression Coefficients -- 3.2.7 Bias Reduction Techniques -- 3.3 Pseudo-Maximum Likelihood -- 3.3.1 Linear Exponential Families -- 3.3.2 Biased Poisson Maximum Likelihood Inference -- 3.3.3 Robust Poisson Regression -- 3.3.4 Non-Parametric Variance Estimation -- 3.3.5 Poisson Regression and Log-Linear Models -- 3.3.6 Generalized Method of Moments -- 3.4 Sources of Misspecification -- 3.4.1 Mean Function -- 3.4.2 Unobserved Heterogeneity -- 3.4.3 Measurement Error -- 3.4.4 Dependent Process -- 3.4.5 Selectivity -- 3.4.6 Simultaneity and Endogeneity -- 3.4.7 Underreporting -- 3.4.8 Excess Zeros -- 3.4.9 Variance Function -- 3.5 Testing for Misspecification -- 3.5.1 Classical Specification Tests -- 3.5.2 Regression Based Tests -- 3.5.3 Goodness-of-Fit Tests --T.

Summary: Surveys statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. This book presents Poisson regression model. It discusses testing and estimation from frequentist and Bayesian perspectives.

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Barcode
E-Book	Dr. S. R. Lasker Library, EWU E-book	Non-fiction	330 WIE 2008 (Browse shelf(Opens below))		Not for loan
Text	Dr. S. R. Lasker Library, EWU Reserve Section	Non-fiction	330 WIE 2008 (Browse shelf(Opens below))		Not For Loan	27315
Text	Dr. S. R. Lasker Library, EWU Circulation Section	Non-fiction	330 WIE 2008 (Browse shelf(Opens below))	C-2	Available	28152.00

Total holds: 0

Includes bibliographical references (p. [299]-319) and indexes.

TOC Cover --
Preface --
Contents --
1 Introduction --
1.1 Poisson Regression Model --
1.2 Examples --
1.3 Organization of the Book --
2 Probability Models for Count Data --
2.1 Introduction --
2.2 Poisson Distribution --
2.2.1 Definitions and Properties --
2.2.2 Genesis of the Poisson Distribution --
2.2.3 Poisson Process --
2.2.4 Generalizations of the Poisson Process --
2.2.5 Poisson Distribution as a Binomial Limit --
2.2.6 Exponential Interarrival Times --
2.2.7 Non-Poissonness --
2.3 Further Distributions for Count Data --
2.3.1 Negative Binomial Distribution --
2.3.2 Binomial Distribution --
2.3.3 Logarithmic Distribution --
2.3.4 Summary --
2.4 Modified Count Data Distributions --
2.4.1 Truncation --
2.4.2 Censoring and Grouping --
2.4.3 Altered Distributions --
2.5 Generalizations --
2.5.1 Mixture Distributions --
2.5.2 Compound Distributions --
2.5.3 Birth Process Generalizations --
2.5.4 Katz Family of Distributions --
2.5.5 Additive Log-Differenced Probability Models --
2.5.6 Linear Exponential Families --
2.5.7 Summary --
2.6 Distributions for Over- and Underdispersion --
2.6.1 Generalized Event Count Model --
2.6.2 Generalized Poisson Distribution --
2.6.3 Poisson Polynomial Distribution --
2.6.4 Double Poisson Distribution --
2.6.5 Summary --
2.7 Duration Analysis and Count Data --
2.7.1 Distributions for Interarrival Times --
2.7.2 Renewal Processes --
2.7.3 Gamma Count Distribution --
2.7.4 Duration Mixture Models --
3 Poisson Regression --
3.1 Specification --
3.1.1 Introduction --
3.1.2 Assumptions of the Poisson Regression Model --
3.1.3 Ordinary Least Squares and Other Alternatives --
3.1.4 Interpretation of Parameters --
3.1.5 Period at Risk --
3.2 Maximum Likelihood Estimation --
3.2.1 Introduction --
3.2.2 Likelihood Function and Maximization --
3.2.3 Newton-Raphson Algorithm --
3.2.4 Properties of the Maximum Likelihood Estimator --
3.2.5 Estimation of the Variance Matrix --
3.2.6 Approximate Distribution of the Poisson Regression Coefficients --
3.2.7 Bias Reduction Techniques --
3.3 Pseudo-Maximum Likelihood --
3.3.1 Linear Exponential Families --
3.3.2 Biased Poisson Maximum Likelihood Inference --
3.3.3 Robust Poisson Regression --
3.3.4 Non-Parametric Variance Estimation --
3.3.5 Poisson Regression and Log-Linear Models --
3.3.6 Generalized Method of Moments --
3.4 Sources of Misspecification --
3.4.1 Mean Function --
3.4.2 Unobserved Heterogeneity --
3.4.3 Measurement Error --
3.4.4 Dependent Process --
3.4.5 Selectivity --
3.4.6 Simultaneity and Endogeneity --
3.4.7 Underreporting --
3.4.8 Excess Zeros --
3.4.9 Variance Function --
3.5 Testing for Misspecification --
3.5.1 Classical Specification Tests --
3.5.2 Regression Based Tests --
3.5.3 Goodness-of-Fit Tests --T.

Surveys statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. This book presents Poisson regression model. It discusses testing and estimation from frequentist and Bayesian perspectives.

Sagar Shahanawaz

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