Econometric Analysis Of Count Data
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Econometric Analysis Of Count Data
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Author : Rainer Winkelmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Econometric Analysis Of Count Data written by Rainer Winkelmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Business & Economics categories.
Graduate students and researchers are provided with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fourth edition contains several new sections, for example on nonnested hurdle models, quantile regression and on software. Many other sections have been entirely rewritten and extended.
Econometric Analysis Of Count Data
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Author : Rainer Winkelmann
language : en
Publisher:
Release Date : 2000
Econometric Analysis Of Count Data written by Rainer Winkelmann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Econometrics categories.
The primary objective of this book is to provide an introduction to the econometric modeling of count data for graduate students and researchers. It should serve anyone whose interest lies either in developing the field fur ther, or in applying existing methods to empirical questions. Much of the material included in this book is not specific to economics, or to quantita tive social sciences more generally, but rather extends to disciplines such as biometrics and technometrics. Applications are as diverse as the number of congressional budget vetoes, the number of children in a household, and the number of mechanical defects in a production line. The unifying theme is a focus on regression models in which a dependent count variable is modeled as a function of independent variables which mayor may not be counts as well. The modeling of count data has come of age. Inclusion of some of the fundamental models in basic textbooks, and implementation on standard computer software programs bear witness to that. Based on the standard Poisson regression model, numerous extensions and alternatives have been developed to address the common challenges faced in empirical modeling (unobserved heterogeneity, selectivity, endogeneity, measurement error, and dependent observations in the context of panel data or multivariate data, to name but a few) as well as the challenges that are specific to count data (e. g. , over dispersion and underdispersion).
Econometric Analysis Of Count Data
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Author : Rainer Winkelmann
language : en
Publisher:
Release Date : 2003
Econometric Analysis Of Count Data written by Rainer Winkelmann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Econometrics categories.
The book provides graduate students and researchers with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fourth edition contains several new sections, for example on nonnested hurdle models, quantile regression and on software. Many other sections have been entirely rewritten and extended.
Regression Analysis Of Count Data
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Author : A. Colin Cameron
language : en
Publisher: Cambridge University Press
Release Date : 1998-09-28
Regression Analysis Of Count Data written by A. Colin Cameron and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-09-28 with Business & Economics categories.
This analysis provides a comprehensive account of models and methods to interpret frequency data.
Regression Analysis Of Count Data
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Author : A. Colin Cameron
language : en
Publisher: Cambridge University Press
Release Date : 2013-05-27
Regression Analysis Of Count Data written by A. Colin Cameron and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-27 with Business & Economics categories.
Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.
Regression Analysis Of Count Data
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Author : Adrian Colin Cameron
language : en
Publisher: Cambridge University Press
Release Date : 2013-05-27
Regression Analysis Of Count Data written by Adrian Colin Cameron and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-27 with Business & Economics categories.
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.
The Econometric Analysis Of Count Data
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Author : Adrian Colin Cameron
language : en
Publisher:
Release Date : 1998-07
The Econometric Analysis Of Count Data written by Adrian Colin Cameron and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-07 with Econometrics categories.
The Econometric Analysis Of Count Data
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Author : Colin Cameron
language : en
Publisher:
Release Date : 1998-07
The Econometric Analysis Of Count Data written by Colin Cameron and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-07 with Econometrics categories.
Econometric Analysis Of Non Standard Count Data
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Author : Ryan T. Godwin
language : en
Publisher:
Release Date : 2012
Econometric Analysis Of Non Standard Count Data written by Ryan T. Godwin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
This thesis discusses various issues in the estimation of models for count data. In the first part of the thesis, we derive an analytic expression for the bias of the maximum likelihood estimator (MLE) of the parameter in a doubly-truncated Poisson distribution, which proves highly effective as a means of bias correction. We explore the circumstances under which bias is likely to be problematic, and provide some indication of the statistical significance of the bias. Over a range of sample sizes, our method outperforms the alternative of bias correction via the parametric bootstrap. We show that MLEs obtained from sample sizes which elicit appreciable bias also have sampling distributions which are unsuited to be approximated by large-sample asymptotics, and bootstrapping confidence intervals around our bias-adjusted estimator is preferred, as two tiers of bootstrapping may incur a heavy computational burden. Modelling count data where the counts are strictly positive is often accomplished using a positive Poisson distribution. Inspection of the data sometimes reveals an excess of ones, analogous to zero-inflation in a regular Poisson model. The latter situation has well developed methods for modelling and testing, such as the zero-inflated Poisson (ZIP) model, and a score test for zero-inflation in a ZIP model. The issue of count inflation in a positive Poisson distribution does not seem to have been considered in a similar way. In the second part of the thesis, we propose a one-inflated positive Poisson (OIPP) model, and develop a score test to determine whether there are "too many" ones for a positive Poisson model to fit well. We explore the performance of our score test, and compare it to a likelihood ratio test, via Monte Carlo simulation. We find that the score test performs well, and that the OIPP model may be useful in many cases. The third part of the thesis considers the possibility of one-inflation in zero-truncated data, when overdispersion is present. We propose a new model to deal with such a phenomenon, the one-inflated zero-truncated negative binomial (OIZTNB) model. The finite sample properties of the maximum likelihood estimators for the parameters of such a model are discussed. This Chapter considers likelihood ratio tests which assist in specifying the OIZTNB model, and investigates the finite sample properties of such tests. The OIZTNB model is illustrated using the medpar data set, which describes the hospital length of stay for a set of patients in Arizona. This is a data set that is widely used to highlight the merits of the zero-truncated negative binomial (ZTNB) model. We find that our OIZTNB model fits the data better than does the ZTNB model, and this leads us to conclude that the data are generated by a one-inflated process.
Econometric Analysis Of Panal Data
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Author : Badi H. Baltagi
language : en
Publisher: John Wiley & Sons
Release Date : 2001-10-31
Econometric Analysis Of Panal Data written by Badi H. Baltagi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-10-31 with Business & Economics categories.
This new edition of this established textbook reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. The book is packed with the most recent empirical examples from panel data literature and includes new data sets. The use of the standard software packages in the field i.e. STATA, LIMDEP, TSP & SAS are illustrated with new examples. The text has also been fully updated with new material on: non-stationary models, unit roots in panels and cointegration, prediction in panels, serial correlation, heteroskedasticity, and new results on GMM in dynamic panel data models. There is also website providing supplementary material for lecturers.