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    <span id="btAsinTitle"></span></h1><h1 class="parseasinTitle"><span id="btAsinTitle">Applied Time Series Econometrics (Hardcover)</span>
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by <a href="http://www.amazon.ca/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-ca&field-author=Helmut%20L%C3%BCtkepohl">Helmut Lütkepohl</a> (Editor), <a href="http://www.amazon.ca/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-ca&field-author=Markus%20Kr%C3%A4tzig">Markus Krätzig</a> (Editor)





"This book discusses tools for the econometric analysis of time series<br/>
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http://www.amazon.ca/gp/product/toc/052183919X/ref=dp_toc?ie=UTF8&;n=916520<br/>
<b class="h1"><br/>
Table of Contents</b><br/>

        
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                <p>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.</p>
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