This book provides an introductory treatment of time series econometrics, a subject that is of key importance to both students and practitioners of economics. It contains material that any serious student of economics and finance should be acquainted with if they are seeking to gain an understanding of a real functioning economy.
This book provides an introductory treatment of time series econometrics, a subject that is of key importance to both students and practitioners of economics. It contains material that any serious student of economics and finance should be acquainted with if they are seeking to gain an understanding of a real functioning economy.
Terence C. Mills is Professor of Applied Statistics and Econometrics at Loughborough University. He has published over 200 articles and books on topics ranging from economic history and the history of econometric thought, through economics, econometrics and finance, to health and well-being, climatology and meteorology.
Inhaltsangabe
1. Introduction 2. Modelling Stationary Time Series: the ARMA Approach 3. Non-stationary Time Series: Differencing and ARIMA Modelling 4. Unit Roots and Related Topics 5. Modelling Volatility using GARCH Processes 6. Forecasting with Univariate Models 7. Modelling Multivariate Time Series: Vector Autoregressions and Granger Causality 8. Cointegration in Single Equations 9. Cointegration in Systems of Equations 10. Extensions and Developments Index
1. Introduction 2. Modelling Stationary Time Series: the ARMA Approach 3. Non-stationary Time Series: Differencing and ARIMA Modelling 4. Unit Roots and Related Topics 5. Modelling Volatility using GARCH Processes 6. Forecasting with Univariate Models 7. Modelling Multivariate Time Series: Vector Autoregressions and Granger Causality 8. Cointegration in Single Equations 9. Cointegration in Systems of Equations 10. Extensions and Developments Index
1. Introduction 2. Modelling Stationary Time Series: the ARMA Approach 3. Non-stationary Time Series: Differencing and ARIMA Modelling 4. Unit Roots and Related Topics 5. Modelling Volatility using GARCH Processes 6. Forecasting with Univariate Models 7. Modelling Multivariate Time Series: Vector Autoregressions and Granger Causality 8. Cointegration in Single Equations 9. Cointegration in Systems of Equations 10. Extensions and Developments Index
1. Introduction 2. Modelling Stationary Time Series: the ARMA Approach 3. Non-stationary Time Series: Differencing and ARIMA Modelling 4. Unit Roots and Related Topics 5. Modelling Volatility using GARCH Processes 6. Forecasting with Univariate Models 7. Modelling Multivariate Time Series: Vector Autoregressions and Granger Causality 8. Cointegration in Single Equations 9. Cointegration in Systems of Equations 10. Extensions and Developments Index
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