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Econometric Analysis of Count Data
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 5th ed. 2008. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-540-78389-3 | ||
| 003 | DE-He213 | ||
| 005 | 20220117054413.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2008 gw | s |||| 0|eng d | ||
| 020 | |a 9783540783893 |9 978-3-540-78389-3 | ||
| 024 | 7 | |a 10.1007/978-3-540-78389-3 |2 doi | |
| 050 | 4 | |a HB139-141 | |
| 072 | 7 | |a KCH |2 bicssc | |
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| 072 | 7 | |a KCH |2 thema | |
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| 100 | 1 | |a Winkelmann, Rainer. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Econometric Analysis of Count Data |h [electronic resource] / |c by Rainer Winkelmann. |
| 250 | |a 5th ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a XVI, 320 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Probability Models for Count Data -- Poisson Regression -- Unobserved Heterogeneity -- Sample Selection and Endogeneity -- Zeros in Count Data Models -- Correlated Count Data -- Bayesian Analysis of Count Data -- Applications. | |
| 520 | |a 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 fifth edition contains several new topics, including copula functions, Poisson regression for non-counts, additional semi-parametric methods, and discrete factor models. Other sections have been reorganized, rewritten, and extended. . | ||
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Statistics . | |
| 650 | 1 | 4 | |a Econometrics. |
| 650 | 2 | 4 | |a Statistics in Business, Management, Economics, Finance, Insurance. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783540847175 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642096402 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540776482 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-540-78389-3 |z Texto Completo |
| 912 | |a ZDB-2-SBE | ||
| 912 | |a ZDB-2-SXEF | ||
| 950 | |a Business and Economics (SpringerNature-11643) | ||
| 950 | |a Economics and Finance (R0) (SpringerNature-43720) | ||