This book is a practical guide to selecting and applying the most appropriate time series model and analysis of data sets using EViews. After introducing EViews workfiles and how to carry out descriptive data analysis, the book goes on to describe various models in detail (continuous growth, discontinuous growth, seemingly causal models, special cases of regression models, ARCH and GARCH models), all illustrated with a rich variety of examples and accompanied by helpful notes. Additional testing hypotheses are also explored and finally extension to a general form of nonlinear time series model is examined. Designed as a special guide for students and less experienced researchers it is a perfect complement to more theoretical books presenting statistical or econometric models for time series data.

ContentsPrefaceList Of TablesList Of FiguresChapter 1: Eviews Workfile And Descriptive Data Analysis1.1 What Is The Eviews Workfile?1.2 Basic Options In Eviews1.3 Creating A Workfile1.4 Illustrative Data Analysis1.5 Special Notes And Comments1.6 Statistics As A Sample SpaceChapter 2: Continuous Growth Models2.1 Introduction2.2 Classical Growth Models2.3 Autoregressive Growth Models2.4. Residual Tests2.5 Bounded Autoregressive Growth Models2.6 Lagged Variables Or Autoregressive Growth Models2.7 Polynomial Growth Model2.8. Growth Models With Exogenous Variables2.9. A Taylor Series Approximation Model2.10 Alternative Univariate Growth Models2.11 Multivariate Growth Models2.12. Multivariate Ar(P) Glm With Trend2.13. Generalized Multivariate Models With Trend2.14 Special Notes And Comments2.15 Alternative Multivariate Models With Trend2.16. Generalized Multivariate ModelsWith Time-Related-EffectsChapter 3: Discontinuous Growth Models3.1 Introduction3.2. Piecewise Growth Models3.3 Piecewise S-Shape Growth Models3.4 Two-Pieces Polynomial Bounded Growth Models3.5 Discontinuous Translog Linear Ar(1) Growth Models.3.6 Alternative Discontinuous Growth Models3.7 Stability Test3.8 Generalized Discontinuous Models With Trend3.9 General Two-Pieces Models With Time-Related Effects3.10. Multivariate Models By States And Time Periods10.2 Not Recommended ModelsChapter 4: Seemingly Causal Models4.1 Introduction4.2 Statistical Analysis Based On Single Time Series4.3 Bivariate Seemingly Causal Models4.4 Trivariate Seemingly Causal Models4.5 System Equations Based On Trivariate Time Series4.6. General System Of Equations4.7 Seemingly Causal Models With Dummy Variables4.8. General Discontinuous Seemingly Causal Models4.9. Additional Selected Seemingly Causal Models4.10. Final Notes In Developing ModelsChapter 5: Special Cases Of Regression Models5.1. Introduction5.2 Specific Cases Of Growth Curve Models5.3 Seemingly Causal Models5.4 Lagged Variable ModelsAnd The Autoregresive Model5.5 Cases Based On The Us Domestic Price Of Copper5.6 Return Rate Models5.7 Cases Based On The Basics WorkfileChapter 6: Var And System Estimation Methods6.1. Introduction6.2 The Var Models6.3 The Vector Error Correction Models6.4 Special Notes And CommentsChapter 7: Instrumental Variables Models7.1. Introduction7.2 Should We Apply Instrumental Models?7.3 Residual Analysis In Developing Instrumental Models7.4 System Equation With Instrumental Variables7.3 Selected Cases Based On The Us_Dpoc Data7.6 Intrumentals Models With Time-Related-Effects7.3 Intrumental Seemingly Causal Models7.8 Multivariate Instrumental Models, Based On The Us_Dpoc7.9. Further Extension Of The Instrumental ModelsChapter 8: Arch Models8.1 Introduction8.2 The Options Of Arch Models8.3 Simple Arch Models8.4. Acrh Models With Exogenous Variables8.5 Alternative Garch Variance SeriesChapter 9: Additional Testing Hypotheses9.1. Introduction9.2. The Unit Root Tests9.3 The Omitted Variables Tests9.4. Redundant Variables Test (Rv-Test)9.5 Non-Nested Test (Nn-Test)9.6 The Ramsey'S Reset TestChapter 10: Nonlinear Least Squares Models10.1 Introduction10.2 Classical Growth Models10.3 Generalized Cobb-Douglas Models10.3 Generalized Ces Models10.4 Special Notes And Comments10.5 Other Nls ModelsChapter 11: Nonparametric Estimation Methods11.1 What Is The Nonparamtric Data Analysis11.2 Basic Moving Average Estimates11.3 Measuring The Best Fit Model11.4. Advanced Moving Average Models11.5. Nonparametric Regression Based On Time Series11.6 The Local Polynomial Kernel Fit Regression11.7 Nonparametric Growth Models