LEADER 03846nam 22006135 450 001 9910983310403321 005 20250216115531.0 010 $a9783031729102 010 $a3031729102 024 7 $a10.1007/978-3-031-72910-2 035 $a(CKB)37627812800041 035 $a(MiAaPQ)EBC31921680 035 $a(Au-PeEL)EBL31921680 035 $a(DE-He213)978-3-031-72910-2 035 $a(OCoLC)1503843199 035 $a(EXLCZ)9937627812800041 100 $a20250216d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDynamic Econometrics $eModels and Applications /$fby Francis J. Bismans, Olivier Damette 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Palgrave Macmillan,$d2025. 215 $a1 online resource (560 pages) 311 08$a9783031729096 311 08$a3031729099 327 $a1. 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. 330 $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. 606 $aEconometrics 606 $aSocial sciences$xMathematics 606 $aRegression analysis 606 $aEconometrics 606 $aQuantitative Economics 606 $aMathematics in Business, Economics and Finance 606 $aLinear Models and Regression 615 0$aEconometrics. 615 0$aSocial sciences$xMathematics. 615 0$aRegression analysis. 615 14$aEconometrics. 615 24$aQuantitative Economics. 615 24$aMathematics in Business, Economics and Finance. 615 24$aLinear Models and Regression. 676 $a330.015195 700 $aBismans$b Francis J$01784670 701 $aDamette$b Olivier$01784671 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983310403321 996 $aDynamic Econometrics$94316273 997 $aUNINA