Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and…mehr
Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Lütkepohl, Helmut§Helmut Lütkepohl is Professor of Economics at the European University Institute in Florence, Italy. He is on leave from Humboldt University Berlin where he has been Professor of Econometrics in the Faculty of Economics and Business Administration since 1992. He had previously been Professor of Statistics at the University of Kiel (1987-1992) and the University of Hamburg (1985-1987) and was Visiting Assistant Professor at the University of California, San Diego (1984-85). Professor Lütkepohl is Associate Editor of Econometric Theory, the Journal of Applied Econometrics, Macroeconomic Dynamics, Empirical Economics and Econometric Reviewa. He has published extensively in learned journals and books and is author, co-author and editor of a number of books in econometrics and time series analysis. Professor Lütkepohl is the author of Introduction to Multiple Time Series Analysis (1991) and a Handbook of Matrices (1996). His current teaching and research interests include methodological issues related to the study of nonstationary, integrated time series and the analysis of the transmission mechanism of monetary policy in the Euro area.
Inhaltsangabe
Preface; Notation and abbreviations; List of contributors; Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction; 2. Setting up an econometric project; 3. Getting data; 4. Data handling; 5. Outline of chapters; Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series; 7. Stationary and integrated stochastic processes; 8. Some popular time series models; 9. Parameter estimation; 10. Model specification; 11. Model checking; 12. Unit root tests; 13. Forecasting univariate time series; 14. Examples; 15. Where to go from here; Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction; 17. VARs and VECMs; 18. Estimation; 19. Model specification; 20. Model checking; 21. Forecasting VAR processes and VECMs; 22. Granger-causality analysis; 23. An example; 24. Extensions; Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction; 26. The models; 27. Impulse response analysis; 28. Estimation of structural parameters; 29. Statistical inference for impulse responses; 30. Forecast error variance decomposition; 31. Examples; 32. Conclusions; Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes; 34. Univariate GARCH models; 35. Multivariate GARCH models; Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction; 37. The model; 38. The modelling cycle; 39. Two empirical examples; 40. Final remarks; Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction; 42. Local linear estimation; 43. Bandwidth and lag selection; 44. Diagnostics; 45. Modelling the conditional volatility; 46. Local linear seasonal modelling; 47. Example I: average weekly working hours in the United States; 48. Example II: XETRA dax index; Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi; 50. Numbers, dates and variables in JMulTi; 51. Handling data sets; 52. Selecting, transforming and creating time series; 53. Managing variables in JMulTi; 54. Notes for econometric software developers; 55. Conclusion; References; Index.
Preface Notation and abbreviations List of contributors Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction 2. Setting up an econometric project 3. Getting data 4. Data handling 5. Outline of chapters Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series 7. Stationary and integrated stochastic processes 8. Some popular time series models 9. Parameter estimation 10. Model specification 11. Model checking 12. Unit root tests 13. Forecasting univariate time series 14. Examples 15. Where to go from here Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction 17. VARs and VECMs 18. Estimation 19. Model specification 20. Model checking 21. Forecasting VAR processes and VECMs 22. Granger-causality analysis 23. An example 24. Extensions Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction 26. The models 27. Impulse response analysis 28. Estimation of structural parameters 29. Statistical inference for impulse responses 30. Forecast error variance decomposition 31. Examples 32. Conclusions Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes 34. Univariate GARCH models 35. Multivariate GARCH models Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction 37. The model 38. The modelling cycle 39. Two empirical examples 40. Final remarks Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction 42. Local linear estimation 43. Bandwidth and lag selection 44. Diagnostics 45. Modelling the conditional volatility 46. Local linear seasonal modelling 47. Example I: average weekly working hours in the United States 48. Example II: XETRA dax index Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi 50. Numbers, dates and variables in JMulTi 51. Handling data sets 52. Selecting, transforming and creating time series 53. Managing variables in JMulTi 54. Notes for econometric software developers 55. Conclusion References Index.
Preface; Notation and abbreviations; List of contributors; Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction; 2. Setting up an econometric project; 3. Getting data; 4. Data handling; 5. Outline of chapters; Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series; 7. Stationary and integrated stochastic processes; 8. Some popular time series models; 9. Parameter estimation; 10. Model specification; 11. Model checking; 12. Unit root tests; 13. Forecasting univariate time series; 14. Examples; 15. Where to go from here; Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction; 17. VARs and VECMs; 18. Estimation; 19. Model specification; 20. Model checking; 21. Forecasting VAR processes and VECMs; 22. Granger-causality analysis; 23. An example; 24. Extensions; Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction; 26. The models; 27. Impulse response analysis; 28. Estimation of structural parameters; 29. Statistical inference for impulse responses; 30. Forecast error variance decomposition; 31. Examples; 32. Conclusions; Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes; 34. Univariate GARCH models; 35. Multivariate GARCH models; Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction; 37. The model; 38. The modelling cycle; 39. Two empirical examples; 40. Final remarks; Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction; 42. Local linear estimation; 43. Bandwidth and lag selection; 44. Diagnostics; 45. Modelling the conditional volatility; 46. Local linear seasonal modelling; 47. Example I: average weekly working hours in the United States; 48. Example II: XETRA dax index; Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi; 50. Numbers, dates and variables in JMulTi; 51. Handling data sets; 52. Selecting, transforming and creating time series; 53. Managing variables in JMulTi; 54. Notes for econometric software developers; 55. Conclusion; References; Index.
Preface Notation and abbreviations List of contributors Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction 2. Setting up an econometric project 3. Getting data 4. Data handling 5. Outline of chapters Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series 7. Stationary and integrated stochastic processes 8. Some popular time series models 9. Parameter estimation 10. Model specification 11. Model checking 12. Unit root tests 13. Forecasting univariate time series 14. Examples 15. Where to go from here Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction 17. VARs and VECMs 18. Estimation 19. Model specification 20. Model checking 21. Forecasting VAR processes and VECMs 22. Granger-causality analysis 23. An example 24. Extensions Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction 26. The models 27. Impulse response analysis 28. Estimation of structural parameters 29. Statistical inference for impulse responses 30. Forecast error variance decomposition 31. Examples 32. Conclusions Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes 34. Univariate GARCH models 35. Multivariate GARCH models Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction 37. The model 38. The modelling cycle 39. Two empirical examples 40. Final remarks Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction 42. Local linear estimation 43. Bandwidth and lag selection 44. Diagnostics 45. Modelling the conditional volatility 46. Local linear seasonal modelling 47. Example I: average weekly working hours in the United States 48. Example II: XETRA dax index Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi 50. Numbers, dates and variables in JMulTi 51. Handling data sets 52. Selecting, transforming and creating time series 53. Managing variables in JMulTi 54. Notes for econometric software developers 55. Conclusion References Index.
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