Ethics in Econometrics

A Guide to Research Practice

AvPhilip Hans Franses

Häftad, Engelska, 2024

396 kr

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Beskrivning

Applied econometrics uses the tools of theoretical econometrics and real-word data to develop predictive models and assess economic theories. Due to the complex nature of such analysis, various assumptions are often not understood by those people who rely on it. The danger of this is that economic policies can be assessed favourably to suit a particular political agenda and forecasts can be generated to match the needs of a particular customer. Ethics in Econometrics argues that econometricians need to be aware of potential ethical pitfalls when carrying out their analysis and that they need to be encouraged to avoid them. Using a range of empirical examples and detailed discussions of real cases, this book provides a guide for research practices in econometrics, illustrating why it is imperative that econometricians act ethically in terms of the way they conduct their analysis and treat their data.

Produktinformation

Utgivningsdatum:2024-11-28
Mått:229 x 151 x 20 mm
Vikt:452 g
Format:Häftad
Språk:Engelska
Antal sidor:310
Förlag:Cambridge University Press
ISBN:9781009428071

Utforska kategorier

Mikroekonomi inom Ekonomi och Ledarskap

Mer om författaren

Philip Hans Franses is Professor of Applied Econometrics at the Erasmus University Rotterdam. He is a Fellow of the Journal of Econometrics, Journal of Applied Econometrics, International Journal of Forecasting, and of the Royal Netherlands Academy of Arts and Sciences. He has published monographs and textbooks with Cambridge University Press.

Recensioner i media

'Having a deeper understanding of econometric tools, their use and misuse, is essential for not making misleading conclusions based on 'data evidence.' This excellent textbook introduces the reader to spotting some of the pitfalls in econometric and other data analysis.' Niels Haldrup, Aarhus University

Innehållsförteckning

Introduction; 1. Ethical guidelines; 2. Scientific misconduct; 3. Influential observations; 4. Model selection; 5. Estimation and interpretation; 6. Missing data; 7. Spurious relations; 8. Blinded by the data; 9. Predictability; 10. Adjustment of forecasts; 11. Big data; 12. Algorithms.