Building better econometric models using cross section and panel data /
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| Главный автор: | |
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| Формат: | Электронный ресурс eКнига |
| Язык: | английский |
| Опубликовано: |
New York, New York (222 East 46th Street, New York, NY 10017) :
Business Expert Press,
2014.
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| Редактирование: | First edition. |
| Серии: | 2014 digital library.
Economics collection. |
| Предметы: | |
| Online-ссылка: | An electronic book accessible through the World Wide Web; click to view |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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| Краткий обзор: | Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ--a methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results. |
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| Примечание: | Part of: 2014 digital library. |
| Объем: | 1 online resource (xiii, 98 pages) Also available in print. |
| Формат: | Mode of access: World Wide Web. System requirements: Adobe Acrobat reader. |
| Библиография: | Includes bibliographical references (pages 95-96) and index. |
| ISBN: | 9781606499757 |
| ISSN: | 2163-7628 |
| Доступ: | Access restricted to authorized users and institutions. |