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Time Series Data Analysis Using EViews

I. Gusti Ngurah Agung

ISBN: 978-0-470-82367-5

Hardcover

320 pages

October 2008

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
1.3.1 Creating A Workfile Using Eviews 5 Or 6
1.3.2 Creating Workfile Using Eviews 4
1.4 Illustrative Data Analysis
1.4.1 Basic Descriptive Statistical Summary
1.4.2 Box Plots And Outliers
1.4.3 Descriptive Statistics By Groups
1.4.4 Graphs Over Times
1.4.5 Means Seasonal Growth Curve
1.4.6 Correlation Matrix
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
2.3.1 First-Order Autoregressive Growth Models
2.3.2 Ar(P) Growth Models
2.4. Residual Tests
2.4.1 Hypothesis Of No Serial Correlation
2.4.2 Hypothesis Of Homogeneous Residual Term
2.4.3 Hypothesis Of The Normality Assumption
2.4.4 Correlogram Q-Statistic
2.5 Bounded Autoregressive Growth Models
2.6 Lagged Variables Or Autoregressive Growth Models
2.6.1 The White Estimation Method
2.6.2 The Newey-West Hac Estimation Method
2.6.3 The Akaike Information And Schwarz Criterions
2.6.4 Mixed Lagged Variables-Autoregressive Growth Models
2.7 Polynomial Growth Model
2.7.1 Basic Polynomial Growth Models
2.7.2. Special Polynomial Growth Models
2.8. Growth Models With Exogenous Variables
2.9. A Taylor Series Approximation Model
2.10 Alternative Univariate Growth Models
2.10.1 A More General Growth Model
2.10.2 Translog Additive Growth Models
2.10.2 Some Comments
2.10.3 Growth Model Having Interaction-Factors
2.10.4. Trigonometric Growth Models
2.11 Multivariate Growth Models
2.11.1 The Classical Multivariate Growth Model
2.11.2 Modified Multivariate Growth Models
2.11.3 Ar(1) Multivariate General Growth Models
2.11.4 The S-Shape Multivariate Ar(1) General Growth Models
2.12. Multivariate Ar(P) Glm With Trend
2.13. Generalized Multivariate Models With Trend
2.13.1. The Simplest Multivariate Autoregressive Model
2.13.2 Multivariate AutoregressiveModel With Two-Way Interactions
2.13.3 Multivariate AutoregressiveModel With Three-Way Interactions
2.14 Special Notes And Comments
2.14.1 The True Population Model
2.14.2 Near singular matrix
2.14.3 “To Test Or Not” The Assumptions Of The Error Terms
2.15 Alternative Multivariate Models With Trend
2.15.1 The Lagged Endogenous Variables - First Autoregressive Model With Trend
2.15.2 The Lagged Endogenous Variables-First Autoregressive Model With Exogenous Variables And Trend
2.15.3 A Mixed Lagged Variables – First Autoregressive Model With Trend
2.16. Generalized Multivariate Models
With Time-Related-Effects Chapter 3: Discontinuous Growth Models
3.1 Introduction
3.2. Piecewise Growth Models
3.2.1 Two-Pieces Classical Growth Models
3.3 Piecewise S-Shape Growth Models
3.3.1 Two-Pieces Linear Growth Models
3.4 Two-Pieces Polynomial Bounded Growth Models
3.4.1 Two-Pieces Quadratic Growth Models
3.4.2 A Two-Pieces Third-Degree Bounded Growth Model
3.4.3 A Two-Pieces Generalized Exponential Growth Model
3.5 Discontinuous Translog Linear Ar(1) Growth Models.
3.6 Alternative Discontinuous Growth Models
3.7 Stability Test
3.7.1 Chow’S Breakpoint Test
3.7.2 Chow’S Forecast Test
3.8 Generalized Discontinuous Models With Trend
3.8.1 General Two-Pieces Univariate Models With Trend
3.8.2 Special Notes And Comments
3.8.3 General Two-Pieces Multivariate Models With Trend
3.9 General Two-Pieces Models With Time-Related Effects
3.10. Multivariate Models By States And Time Periods
3.10.1 Alternative Models
10.2 Not Recommended Models Chapter 4: Seemingly Causal Models
4.1 Introduction
4.2 Statistical Analysis Based On Single Time Series
4.2.1 The Mean Model
4.2.2 The Cell-Means Models
4.2.3 The Lagged-Variables Models
4.2.4 Autoregressive Models
4.2.5 Lagged-Variables-Autoregressive Models
4.3 Bivariate Seemingly Causal Models
4.3.1 The Simplest Seemingly Causal Models
4.3.2 SimplestModels In Three Dimensional Space
4.3.3 General Univariate LVAR(p,q) Seemingly Causal Model
4.4 Trivariate Seemingly Causal Models
4.4.1 Simple Models In Three Dimensional Space
4.5 System Equations Based On Trivariate Time Series
4.6. General System Of Equations
4.7 Seemingly Causal Models With Dummy Variables
4.7.1 Homogeneous Time Series Models
4.7.2 Heterogeneous Time Series Models
4.8. General Discontinuous Seemingly Causal Models
4.9. Additional Selected Seemingly Causal Models
4.9.1 A Third Degree Polynomial Function
4.9.2 A Three Dimensional Bounded Semilog Linear Model
4.9.3 Time Series Cobb-Douglas Models
4.9.4 Ces Time Series Models
4.10. Final Notes In Developing Models
4.10.1 Experts’ Judgment
4.10.2. Other Unexpected Models Chapter 5: Special Cases Of Regression Models
5.1. Introduction
5.2 Specific Cases Of Growth Curve Models
5.2.1 Basic Polynomial Model
5.2.2 An Ar(1) Regression Model
5.2.3 Heteroskedasticity-Consistent Covariance (White)
5.3 Seemingly Causal Models
5.3.1 Autoregressive Models
5.4 Lagged Variable Models
5.4.1 The Basic Lagged-Variables Model
And The Autoregresive Model
5.4.2 Some Notes
5.4.3 Generalized Lagged Variable-Autoregressive Model
5.5 Cases Based On The Us Domestic Price Of Copper
5.5.1 Graphical Representation
5.5.2 Seemingly Causal Model
5.5.2.1 Simplest Seemingly Causal Models
5.5.2 Generalized Translog Linear Model
5.5.3 Constant Elasticity Of Substitution Models
5.5.4 Models For The First Difference Of An Endogenous Variable
5.5.5 Unexpected Findings
5.5.6 Multivariate Linear Seemingly Causal Models
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
6.2.1 The Basic Var Model
6.2.2 The Var Models With Exogenous Variables
6.2.3 Cases Based On The Demo_Modified Workfile
6.2.3 The Var Models With Dummy Variables
6.2.4 Selected Var Models Based On The Us Domestic Price Of Copper Data
6.2.4.1 Application Of Continuous Var Models With Trend
6.2.4.2 Application Of The Var Seemingly Causal Models
6.3 The Vector Error Correction Models
6.3.1 The Basic Vec Model
6.3.3 General Equation Of The Basic Vec Models
6.3.2 The Vec Models With Exogenous Variables
6.3.3 Some Notes And Comments
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
7.3.1 Testing Hypothesis Corresponding To The Instrumental Models
7.3.2 Graphical Representation Of The Residual Series
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
7.8.1 Simple Multivariate Instrumental Models
7.8.2 Multivariate Instrumental Models
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
8.3.1. Simple Arch Models
8.3.2. Special Notes On The Arch Models
8.4. Acrh Models With Exogenous Variables
8.4.1. Arch Models With One Exogenous Variable
8.4.2. Arch Models With Two Exogenous Variables
8.4.3 Advanced Arch Models
8.5 Alternative Garch Variance Series
8.5.1 General Garch Variance Series For The Garch/Tarch Model
8.5.2 General Garch Variance Series For The Egarch Model
8.5.3 General Garch Variance Series For The Parch Model
8.5.4 General Garch Variance Series For The Component Arch(1,1) Model
8.5.5 Special Notes On The Garch Variance Series Chapter 9: Additional Testing Hypotheses
9.1. Introduction
9.2. The Unit Root Tests
9.2.1. Simple Unit Root Test
9.2.3 Comments On 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
10.3.1. Cases Based On The Demo.Wf1
10.3.2. Cases Based On The Basic.Wf1
10.3.3 Cases Based On The Us_Dpoc Data
10.3 Generalized Ces Models
10.4 Special Notes And Comments
10.5 Other Nls Models
10.5.1 Cases Based On The Demo.Wf1 Chapter 11: Nonparametric Estimation Methods
11.1 What Is The Nonparamtric Data Analysis
11.2 Basic Moving Average Estimates
11.2.1 Simple Moving Average Estimates
11.2.2 The Weighted Moving Average Estimates
11.3 Measuring The Best Fit Model
11.4. Advanced Moving Average Models
11.4.1 The Moving Average Models
11.4.3 The Arma Models With Covariates
11.5. Nonparametric Regression Based On Time Series
11.5.1 The Hardle’S Moving Average Models
11.5.2 The Nearest Neighbor Fit
11.5.3 Mathematical Background Of The Nearest Neighbor Fit
11.6 The Local Polynomial Kernel Fit Regression
11.7 Nonparametric Growth Models
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