Dynamic Econometrics: Models and Applications - Paperback

by Francis J. Bismans (Author), Olivier Damette (Author)

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.

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"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 Ros?rio 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.

Author Biography

Francis Bismans is Professor in Economics and Statistics, University of Lorraine, France.

Olivier Damette is Professor in Economics, University of Lorraine, France.