Time Series Data Analysis Using Eviews

Informationen zum Autor 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. Klappentext 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 totime 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 setsAn 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. Zusammenfassung This book is a practical guide to selecting and applying the most appropriate time series model and analysis of data sets using EViews. Inhaltsverzeichnis Preface. 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. 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. 3 Discontinuous growth models. 3.1 Introduction. 3.2 Piecewise growth models. 3.3 Piecewise S-shape growth models. 3.4 Two-piece polynomi...

List of contents

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

Product details

• Authors: Agung, I Gusti Ngurah Agung, I. G. N. Agung, I. Gusti Ngurah Agung, Agung I. Gusti Ngurah
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 17.02.2009
• EAN: 9780470823675
• ISBN: 978-0-470-82367-5
• No. of pages: 632
• Dimensions: 160 mm x 235 mm x 37 mm
• Series: Statistics in Practice; Statistics in Practice
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business; Statistik, Volkswirtschaftslehre, Economics, Statistics, Ökonometrie, Zeitreihen, Zeitreihenanalyse, Econometrics, Time Series, Statistics for Finance, Business & Economics, Finanz- u. Wirtschaftsstatistik