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Estimation of Panel Data Models with Individual Effects (Paperback)

Megersa Tadesse Jirata, Chelule Cheruiyot, Odhiambo Romanus

ISBN 10: 3659429414 / ISBN 13: 9783659429415
Editorial: LAP Lambert Academic Publishing, 2016
Nuevos Condición: New Encuadernación de tapa blanda
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Language: English . Brand New Book. This book focused on the panel data model estimators. Panel data is a continuously developing field. Panel data combine cross-sectional and time-series data and, therefore, provide a more appealing structure of data analysis than either cross sectional or time-series data, alone. Panel data analysis has many advantages over analysis using time-series and cross-sectional data alone. For example, the increased sample size due to the utilization of cross-sectional and time-series data improves the accuracy of model parameters estimates due to a greater number of degrees of freedom and less multicollinearity compared to either cross-section or time-series data alone. Additionally, since panel data contain information on both the inter-temporal dynamics and the individuality of entities, it controls for the effect of missing variables on the estimation results. This book targeted at undergraduate, postgraduate students and other researchers who want to further their study on panel data models. N° de ref. de la librería KNV9783659429415

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Título: Estimation of Panel Data Models with ...

Editorial: LAP Lambert Academic Publishing

Año de publicación: 2016

Encuadernación: Paperback

Condición del libro:New

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This book focused on the panel data model estimators. Panel data is a continuously developing field. Panel data combine cross-sectional and time-series data and, therefore, provide a more appealing structure of data analysis than either cross sectional or time-series data, alone. Panel data analysis has many advantages over analysis using time-series and cross-sectional data alone. For example, the increased sample size due to the utilization of cross-sectional and time-series data improves the accuracy of model parameters' estimates due to a greater number of degrees of freedom and less multicollinearity compared to either cross-section or time-series data alone. Additionally, since panel data contain information on both the inter-temporal dynamics and the individuality of entities, it controls for the effect of missing variables on the estimation results. This book targeted at undergraduate, postgraduate students and other researchers who want to further their study on panel data models.

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