Time Series Data Analysis Using EViews

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ISBN: 9780470823675 | 0470823674
Cover: Hardcover
Copyright: 10/31/2008

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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.

Contents
Preface
List Of Tables
List Of Figures
Eviews Workfile And Descriptive Data Analysis
What Is The Eviews Workfile?
Basic Options In Eviews
Creating A Workfile
Creating A Workfile Using Eviews 5 Or 6
Creating Workfile Using Eviews 4
Illustrative Data Analysis
Basic Descriptive Statistical Summary
Box Plots And Outliers
Descriptive Statistics By Groups
Graphs Over Times
Means Seasonal Growth Curve
Correlation Matrix
Special Notes And Comments
Statistics As A Sample Space
Continuous Growth Models
Introduction
Classical Growth Models
Autoregressive Growth Models
First-Order Autoregressive Growth Models
Ar(P) Growth Models
Residual Tests
Hypothesis Of No Serial Correlation
Hypothesis Of Homogeneous Residual Term
Hypothesis Of The Normality Assumption
Correlogram Q-Statistic
Bounded Autoregressive Growth Models
Lagged Variables Or Autoregressive Growth Models
The White Estimation Method
The Newey-West Hac Estimation Method
The Akaike Information And Schwarz Criterions
Mixed Lagged Variables-Autoregressive Growth Models
Polynomial Growth Model
Basic Polynomial Growth Models
Special Polynomial Growth Models
Growth Models With Exogenous Variables
A Taylor Series Approximation Model
Alternative Univariate Growth Models
A More General Growth Model
Translog Additive Growth Models
Some Comments
Growth Model Having Interaction-Factors
Trigonometric Growth Models
Multivariate Growth Models
The Classical Multivariate Growth Model
Modified Multivariate Growth Models
Ar(1) Multivariate General Growth Models
The S-Shape Multivariate Ar(1) General Growth Models
Multivariate Ar(P) Glm With Trend
Generalized Multivariate Models With Trend
The Simplest Multivariate Autoregressive Model
Multivariate Autoregressive Model With Two-Way Interactions
Multivariate Autoregressive Model With Three-Way Interactions
Special Notes And Comments
The True Population Model
Near singular matrix
öTo Test Or Notö The Assumptions Of The Error Terms
Alternative Multivariate Models With Trend
The Lagged Endogenous Variables - First Autoregressive Model With Trend
The Lagged Endogenous Variables-First Autoregressive Model With Exogenous Variables And Trend
A Mixed Lagged Variables û First Autoregressive Model With Trend
Generalized Multivariate Models
With Time-Related-Effects
Discontinuous Growth Models
Introduction
Piecewise Growth Models
Two-Pieces Classical Growth Models
Piecewise S-Shape Growth Models
Two-Pieces Linear Growth Models
Two-Pieces Polynomial Bounded Growth Models
Two-Pieces Quadratic Growth Models
A Two-Pieces Third-Degree Bounded Growth Model
A Two-Pieces Generalized Exponential Growth Model
Discontinuous Translog Linear Ar(1) Growth Models
Alternative Discontinuous Growth Models
Stability Test
ChowÆS Breakpoint Test
ChowÆS Forecast Test
Generalized Discontinuous Models With Trend
General Two-Pieces Univariate Models With Trend
Special Notes And Comments
General Two-Pieces Multivariate Models With Trend
General Two-Pieces Models With Time-Related Effects
Multivariate Models By States And Time Periods
Alternative Models
Not Recommended Models
Seemingly Causal Models
Introduction
Statistical Analysis Based On Single Time Series
The Mean Model
The Cell-Means Models
The Lagged-Variables Models
Autoregressive Models
Lagged-Variables-Autoregressive Models
Bivariate Seemingly Causal Models
The Simplest Seemingly Causal Models
Simplest Models In Three Dimensional Space
General Univariate LVAR(p,q) Seemingly Causal Model
Trivariate Seemingly Causal Models
Simple Models In Three Dimensional Space
System Equations Based On Trivariate Time Series
General System Of Equations
Seemingly Causal Models With Dummy Variables
Homogeneous Time Series Models
Heterogeneous Time Series Models
General Discontinuous Seemingly Causal Models
Additional Selected Seemingly Causal Models
A Third Degree Polynomial Function
A Three Dimensional Bounded Semilog Linear Model
Time Series Cobb-Douglas Models
Ces Time Series Models
Final Notes In Developing Models
ExpertsÆ Judgment
Other Unexpected Models
Special Cases Of Regression Models
Introduction
Specific Cases Of Growth Curve Models
Basic Polynomial Model
An Ar(1) Regression Model
Heteroskedasticity-Consistent Covariance (White)
Seemingly Causal Models
Autoregressive Models
Lagged Variable Models
The Basic Lagged-Variables Model
And The Autoregresive Model
Some Notes
Generalized Lagged Variable-Autoregressive Model
Cases Based On The Us Domestic Price Of Copper
Graphical Representation
Seemingly Causal Model
Simplest Seemingly Causal Models
Generalized Translog Linear Model
Constant Elasticity Of Substitution Models
Models For The First Difference Of An Endogenous Variable
Unexpected Findings
Multivariate Linear Seemingly Causal Models
Return Rate Models
Cases Based On The Basics Workfile
Var And System Estimation Methods
Introduction
The Var Models
The Basic Var Model
The Var Models With Exogenous Variables
Cases Based On The Demo_Modified Workfile
The Var Models With Dummy Variables
Selected Var Models Based On The Us Domestic Price Of Copper Data
Application Of Continuous Var Models With Trend
Application Of The Var Seemingly Causal Models
The Vector Error Correction Models
The Basic Vec Model
General Equation Of The Basic Vec Models
The Vec Models With Exogenous Variables
Some Notes And Comments
Special Notes And Comments
Instrumental Variables Models
Introduction
Should We Apply Instrumental Models?
Residual Analysis In Developing Instrumental Models
Testing Hypothesis Corresponding To The Instrumental Models
Graphical Representation Of The Residual Series
System Equation With Instrumental Variables
Selected Cases Based On The Us_Dpoc Data
Intrumentals Models With Time-Related-Effects
Intrumental Seemingly Causal Models
Multivariate Instrumental Models, Based On The Us_Dpoc
Simple Multivariate Instrumental Models
Multivariate Instrumental Models
Further Extension Of The Instrumental Models
Arch Models
Introduction
The Options Of Arch Models
Simple Arch Models
Simple Arch Models
Special Notes On The Arch Models
Acrh Models With Exogenous Variables
Arch Models With One Exogenous Variable
Arch Models With Two Exogenous Variables
Advanced Arch Models
Alternative Garch Variance Series
General Garch Variance Series For The Garch/Tarch Model
General Garch Variance Series For The Egarch Model
General Garch Variance Series For The Parch Model
General Garch Variance Series For The Component Arch(1,1) Model
Special Notes On The Garch Variance Series
Additional Testing Hypotheses
Introduction
The Unit Root Tests
Simple Unit Root Test
Comments On The Unit Root Tests
The Omitted Variables Tests
Redundant Variables Test (Rv-Test)
Non-Nested Test (Nn-Test)
The RamseyÆS Reset Test
Nonlinear Least Squares Models
Introduction
Classical Growth Models
Generalized Cobb-Douglas Models
Cases Based On The Demo.Wf1
Cases Based On The Basic.Wf1
Cases Based On The Us_Dpoc Data
Generalized Ces Models
Special Notes And Comments
Other Nls Models
Cases Based On The Demo.Wf1
Nonparametric Estimation Methods
What Is The Nonparamtric Data Analysis
Basic Moving Average Estimates
Simple Moving Average Estimates
The Weighted Moving Average Estimates
Measuring The Best Fit Model
Advanced Moving Average Models
The Moving Average Models
The Arma Models With Covariates
11.5. Nonparametric Regression Based On Time Series
The HardleÆS Moving Average Models
The Nearest Neighbor Fit
Mathematical Background Of The Nearest Neighbor Fit
The Local Polynomial Kernel Fit Regression
Nonparametric Growth Models
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Time Series Data Analysis Using EViews

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