Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Contents Preface List Of Tables List Of FiguresChapter 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 SpaceChapter 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 Autoregressive Model With Two-Way Interactions 2.13.3 Multivariate Autoregressive Model 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-EffectsChapter 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#. N° de ref. de la librería
Sinopsis: 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.
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.
About the Author: 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.
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