Jones | Applied Econometrics for Health Economists | Buch | 978-1-84619-171-8 | sack.de

Buch, Englisch, 128 Seiten, Format (B × H): 169 mm x 244 mm, Gewicht: 249 g

Jones

Applied Econometrics for Health Economists

A Practical Guide

Buch, Englisch, 128 Seiten, Format (B × H): 169 mm x 244 mm, Gewicht: 249 g

ISBN: 978-1-84619-171-8
Verlag: CRC Press


"Applied Econometrics for Health Economists" introduces readers to the appropriate econometric techniques for use with different forms of survey data, known collectively as microeconometrics. The book provides a complete illustration of the steps involved in doing microeconometric research. The only study to deal with practical analysis of qualitative and categorical variables, it also emphasises applied work, illustrating the use of relevant computer software applied to large-scale survey datasets. This is a comprehensive reference guide - it contains a glossary of terms, a technical appendix, software appendix, references, and suggestions for further reading. It is concise and easy to read - technical details are avoided in the main text and key terms are highlighted. It is essential reading for health economists as well as undergraduate and postgraduate students of health economics. "Given the extensive use of individual-level survey data in health economics, it is important to understand the econometric techniques available to applied researchers. Moreover, it is just as important to be aware of their limitations and pitfalls. The purpose of this book is to introduce readers to the appropriate econometric techniques for use with different forms of survey data - known collectively as microeconometrics." - Andrew Jones, in the Preface.
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Academic and Professional Practice & Development


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Weitere Infos & Material


Introduction: the evaluation problem and linear regression. The health and lifestyle survey. Binary dependent variables. The ordered probit model. Multinomial models. The bivariate probit model. The selection problem. Endogenous regressors: the evaluation problem revisited. Count data regression. Duration analysis. Panel data.



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