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.
I Gusti Ngurah Agung is a Lecturer and Academic Advisor at the Graduate School of Management, Faculty of Economics at the University of Indonesia. He has been teaching mathematical statistics and applied statistics since 1960 at the Makassar Public University as well as Hassanudin University, Makassar, and since 2006 at the Graduate School of Planning, Strategy and Public Policy, University of Indonesia. Agung has authored more than 10 pocket books in applied statistics (in Indonesian). He holds a BSc in Mathematical Education from Hassanudin University, a Masters in Mathematics from the New Mexico State University and a second Masters in mathematical statistics as well as a PhD in biostatistics from the University of North Carolina at Chapel Hill.
Inhaltsangabe
Contents Preface List Of Tables List Of Figures
Chapter 1: Eviews Workfile And Descriptive Data Analysis 1.1 What Is The Eviews Workfile? 1.2 Basic Options In Eviews 1.3 Creating A Workfile 1.4 Illustrative Data Analysis 1.5 Special Notes And Comments 1.6 Statistics As A Sample Space
Chapter 2: Continuous Growth Models 2.1 Introduction 2.2 Classical Growth Models 2.3 Autoregressive Growth Models 2.4. Residual Tests 2.5 Bounded Autoregressive Growth Models 2.6 Lagged Variables Or Autoregressive Growth Models 2.7 Polynomial Growth Model 2.8. Growth Models With Exogenous Variables 2.9. A Taylor Series Approximation Model 2.10 Alternative Univariate Growth Models 2.11 Multivariate Growth Models 2.12. Multivariate Ar(P) Glm With Trend 2.13. Generalized Multivariate Models With Trend 2.14 Special Notes And Comments 2.15 Alternative Multivariate Models With Trend 2.16. Generalized Multivariate Models With Time-Related-Effects
Chapter 3: Discontinuous Growth Models 3.1 Introduction 3.2. Piecewise Growth Models 3.3 Piecewise S-Shape Growth Models 3.4 Two-Pieces Polynomial Bounded Growth Models 3.5 Discontinuous Translog Linear Ar(1) Growth Models 3.6 Alternative Discontinuous Growth Models 3.7 Stability Test 3.8 Generalized Discontinuous Models With Trend 3.9 General Two-Pieces Models With Time-Related Effects 3.10. Multivariate Models By States And Time Periods 10.2 Not Recommended Models
Chapter 4: Seemingly Causal Models 4.1 Introduction 4.2 Statistical Analysis Based On Single Time Series 4.3 Bivariate Seemingly Causal Models 4.4 Trivariate Seemingly Causal Models 4.5 System Equations Based On Trivariate Time Series 4.6. General System Of Equations 4.7 Seemingly Causal Models With Dummy Variables 4.8. General Discontinuous Seemingly Causal Models 4.9. Additional Selected Seemingly Causal Models 4.10. Final Notes In Developing Models
Chapter 5: Special Cases Of Regression Models 5.1. Introduction 5.2 Specific Cases Of Growth Curve Models 5.3 Seemingly Causal Models 5.4 Lagged Variable Models And The Autoregresive Model 5.5 Cases Based On The Us Domestic Price Of Copper 5.6 Return Rate Models 5.7 Cases Based On The Basics Workfile
Chapter 6: Var And System Estimation Methods 6.1. Introduction 6.2 The Var Models 6.3 The Vector Error Correction Models 6.4 Special Notes And Comments
Chapter 7: Instrumental Variables Models 7.1. Introduction 7.2 Should We Apply Instrumental Models? 7.3 Residual Analysis In Developing Instrumental Models 7.4 System Equation With Instrumental Variables 7.3 Selected Cases Based On The Us_Dpoc Data 7.6 Intrumentals Models With Time-Related-Effects 7.3 Intrumental Seemingly Causal Models 7.8 Multivariate Instrumental Models, Based On The Us_Dpoc 7.9. Further Extension Of The Instrumental Models
Chapter 8: Arch Models 8.1 Introduction 8.2 The Options Of Arch Models 8.3 Simple Arch Models 8.4. Acrh Models With Exogenous Variables 8.5 Alternative Garch Variance Series
Chapter 9: Additional Testing Hypotheses 9.1. Introduction 9.2. The Unit Root Tests 9.3 The Omitted Variables Tests 9.4. Redundant Variables Test (Rv-Test) 9.5 Non-Nested Test (Nn-Test) 9.6 The Ramsey'S Reset Test
Chapter 10: Nonlinear Least Squares Models 10.1 Introduction 10.2 Classical Growth Models 10.3 Generalized Cobb-Douglas Models 10.3 Generalized Ces Models 10.4 Special Notes And Comments 10.5 Other Nls Models
Chapter 11: Nonparametric Estimation Methods 11.1 What Is The Nonparamtric Data Analysis 11.2 Basic Moving Average Estimates 11.3 Measuring The Best Fit Model 11.4. Advanced Moving Average Models 11.5. Nonparametric Regression Based On Time Series 11.6 The Local Polynomial Kernel Fit Regression 11.7 Nonparametric Growth Models
