Dynamic Econometrics (eBook, PDF)
Models and Applications
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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 autoregre...
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.
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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