I. Gusti Ngurah Agung

Time Series Data Analysis Using Eviews

• Gebundenes Buch

Produktbeschreibung

Do you want to recognize the most suitable models for analysis of statistical data sets?
This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets.

The author:

Provides step-by-step directions on how to apply EViews software to time series data analysis
Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae
Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models
Gives over 250 illustrative examples and notes based on the author s own empirical findings, allowing the advantages and limitations of each model to be understood
Describes the theory behind the models in comprehensive appendices
Provides supplementary information and data sets
An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as applied researchers, will also find this book an invaluable resource.

Produktdetails

• Statistics in Practice
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 632
• Erscheinungstermin: 1. April 2009
• Englisch
• Abmessung: 235mm x 157mm x 38mm
• Gewicht: 1026g
• ISBN-13: 9780470823675
• ISBN-10: 0470823674
• Artikelnr.: 25625280

Autorenporträt

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