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Dynamic econometrics = models and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic econometrics/ by Francis J. Bismans, Olivier Damette.
其他題名:	models and applications /
作者:	Bismans, Francis J.
其他作者:	Damette, Olivier.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxii, 349 p. :ill. (some col.), digital ;24 cm.
內容註:	1. General Introduction -- 2. Dynamics in Econometrics -- 3. Estimating the Model -- 4. Testing the Model -- 5. Non-Stationarity and Cointegration -- 6. Specifying the ARDL Model -- 7. Vector Autoregressions -- 8. Panel Data Models -- 9. Non-Stationary Panels -- 10. The Binary Qualitative Model.
Contained By:	Springer Nature eBook
標題:	Econometric models. -
電子資源:	https://doi.org/10.1007/978-3-031-72910-2
ISBN:	9783031729102

Dynamic econometrics = models and applications /
Bismans, Francis J.

Dynamic econometricsmodels and applications /[electronic resource] :by Francis J. Bismans, Olivier Damette. - Cham :Springer Nature Switzerland :2025. - xxii, 349 p. :ill. (some col.), digital ;24 cm.

1. General Introduction -- 2. Dynamics in Econometrics -- 3. Estimating the Model -- 4. Testing the Model -- 5. Non-Stationarity and Cointegration -- 6. Specifying the ARDL Model -- 7. Vector Autoregressions -- 8. Panel Data Models -- 9. Non-Stationary Panels -- 10. The Binary Qualitative Model.

"This book is a bold and confident advance in dynamic econometric theory and practice." I. Litvine, Professor in Statistics, Nelson Mandela University, Port Elizabeth, South Africa "This book is an outstanding contribution to econometrics, coming at a crucial time to fill a significant gap in the field." Maria do Rosário Grossinho, Professor of Analysis and Mathematical Finance ISEG - University of Lisbon Portugal This textbook for advanced econometrics students introduces key concepts of dynamic non-stationary modelling. It discusses all the classic topics in time series analysis and linear models containing multiple equations, as well as covering panel data models, and non-linear models of qualitative variables. The book offers a general introduction to dynamic econometrics and covers topics including non-stationary stochastic processes, unit root tests, Monte Carlo simulations, heteroskedasticity, autocorrelation, cointegration and error correction mechanism, models specification, and vector autoregressions. Going beyond advanced dynamic analysis, the book also meticulously analyses the classical linear regression model (CLRM) and introduces students to estimation and testing methods for the more advanced auto-regressive distributed lag (ARDL) model. The book incorporates worked examples, algebraic explanations and learning exercises throughout. It will be a valuable resource for graduate and postgraduate students in econometrics and quantitative finance as well as academic researchers in this area. Francis Bismans is Professor in Economics and Statistics, University of Lorraine, France. Olivier Damette is Professor in Economics, University of Lorraine, France.

ISBN: 9783031729102

Standard No.: 10.1007/978-3-031-72910-2doiSubjects--Topical Terms:

542933
Econometric models.

LC Class. No.: HB141

Dewey Class. No.: 330.015195

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520 $a "This book is a bold and confident advance in dynamic econometric theory and practice." I. Litvine, Professor in Statistics, Nelson Mandela University, Port Elizabeth, South Africa "This book is an outstanding contribution to econometrics, coming at a crucial time to fill a significant gap in the field." Maria do Rosário Grossinho, Professor of Analysis and Mathematical Finance ISEG - University of Lisbon Portugal This textbook for advanced econometrics students introduces key concepts of dynamic non-stationary modelling. It discusses all the classic topics in time series analysis and linear models containing multiple equations, as well as covering panel data models, and non-linear models of qualitative variables. The book offers a general introduction to dynamic econometrics and covers topics including non-stationary stochastic processes, unit root tests, Monte Carlo simulations, heteroskedasticity, autocorrelation, cointegration and error correction mechanism, models specification, and vector autoregressions. Going beyond advanced dynamic analysis, the book also meticulously analyses the classical linear regression model (CLRM) and introduces students to estimation and testing methods for the more advanced auto-regressive distributed lag (ARDL) model. The book incorporates worked examples, algebraic explanations and learning exercises throughout. It will be a valuable resource for graduate and postgraduate students in econometrics and quantitative finance as well as academic researchers in this area. Francis Bismans is Professor in Economics and Statistics, University of Lorraine, France. Olivier Damette is Professor in Economics, University of Lorraine, France.
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