Agung

Cross Section and Experimental Data Analysis Using EViews (eBook, ePUB)

• Format: ePub

Produktbeschreibung

A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews. "This book is a reflection of the vast experience and knowledge of the author. It is a useful reference for students and practitioners dealing with cross sectional data analysis ... The strength of the book lies in its wealth of material and well structured guidelines ..." Prof. Yohanes Eko Riyanto, Nanyang Technological University, Singapore "This is superb and brilliant. Prof. Agung has skilfully transformed his best experiences into new knowledge ... creating a new way of understanding data analysis." Dr. I Putu Gede Ary Suta, The Ary Suta Center, Jakarta Basic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less experienced researchers. This book addresses this issue by providing a hands-on practical guide to conducting data analysis using EViews combined with a variety of illustrative models (and their extensions). Models having numerically dependent variables based on a cross-section data set (such as univariate, multivariate and nonlinear models as well as non-parametric regressions) are concentrated on. It is shown that a wide variety of hypotheses can easily be tested using EViews. Cross Section and Experimental Data Analysis Using EViews: * Provides step-by-step directions on how to apply EViews to cross section data analysis - from multivariate analysis and nonlinear models to non-parametric regression * Presents a method to test for all possible hypotheses based on each model * Proposes a new method for data analysis based on a multifactorial design model * Demonstrates that statistical summaries in the form of tabulations are invaluable inputs for strategic decision making * Contains 200 examples with special notes and comments based on the author's own empirical findings as well as over 400 illustrative outputs of regressions from EViews * Techniques are illustrated through practical examples from real situations * Comes with supplementary material, including work-files containing selected equation and system specifications that have been applied in the book This user-friendly introduction to EViews is ideal for Advanced undergraduate and graduate students taking finance, econometrics, population, or public policy courses, as well as applied policy researchers.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 584
• Erscheinungstermin: 15. Februar 2011
• Englisch
• ISBN-13: 9780470828458
• Artikelnr.: 37299694

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, since 1987 at the Faculty of Economics, University of Indonesia, and since 2006 at the Graduate School of Planning, Strategy and Public Policy, University of Indonesia. Areas of interest include population studies, education, public health, economics, management and finance. Agung has authored one applied statistics textbook in English and more than 10 pocket books 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

Preface. 1 Misinterpretation of Selected Theoretical Concepts of
Statistics. 1.1 Introduction. 1.2 What is a Population? 1.3 A Sample and
Sample Space. 1.4 Distribution of a Random Sample Space. 1.5 What is a
Random Variable? 1.6 Theoretical Concept of a Random Sample. 1.7 Does a
Representative Sample Really Exist? 1.8 Remarks on Statistical Powers and
Sample Sizes. 1.9 Hypothesis and Hypothesis Testing. 1.10 Groups of
Research Variables. 1.11 Causal Relationship between Variables. 1.12
Misinterpretation of Selected Statistics. 2 Simple Statistical Analysis but
Good for Strategic Decision Making. 2.1 Introduction. 2.2 A Single Input
for Decision Making. 2.3 Data Transformation. 2.4 Biserial Correlation
Analysis. 2.5 One-Way Tabulation of a Variable. 2.6 Two-Way Tabulations.
2.7 Three-Way Tabulation. 2.8 Special Notes and Comments. 2.9 Special Cases
of the N-Way Incomplete Tables. 2.10 Partial Associations. 2.11 Multiple
Causal Associations Based on Categorical Variables. 2.12 Seemingly Causal
Model Based on Categorical Variables. 2.13 Alternative Descriptive
Statistical Summaries. 2.14 How to Present Descriptive Statistical Summary?
2.15 General Seemingly Causal Model. 2.16 Empirical Studies Presenting
Descriptive Statistical Summaries. 3 One-Way Proportion Models. 3.1
Introduction. 3.2 One-Way Proportion Models Based on a 2 2 Table. 3.3
Binary Choice Models Based on a K 2 Table. 3.4 Binary Logit Models Based on
N-Way Tabulation. 3.5 General Binary Choice Models. 3.6 Special Notes and
Comments. 3.7 Association between Categorical Variables. 3.8 One-Way Binary
Choice Models Based on N-Way Tabulation. 3.9 Special Notes and Comments on
Binary Choice Models. 4 N-Way Cell-Proportion Models. 4.1 Introduction. 4.2
The N-Way Tabulation of Proportions. 4.3 The 2 2 Factorial Model of
Proportions. 4.4 I J Factorial Models of Proportions. 4.5 Multifactorial
Cell-Proportion Model. 4.6 Presenting the Statistical Summary. 5 N-Way
Cell-Mean Models. 5.1 Introduction. 5.2 One-Way Multivariate Cell-Mean
Models. 5.3 N-Way Multivariate Cell-Mean Models. 5.4 Equality Test by
Classification. 5.5 Testing Weighted Means Differences. 5.6 Descriptive
Statistical Summary. 6 Multinomial Choice Models with Categorical Exogenous
Variables. 6.1 Introduction. 6.2 Multinomial Choice Models. 6.3 Ordered
Choice Models. 6.4 Concordance-Discordance Measure of Association. 6.5
Multifactorial Ordered Choice Models. 6.6 Multilevel Choice Models. 6.7
Special Notes on the Multinomial Logit Model. 6.8 Selected Population
Studies Using Multinomial Choice Models. 7 General Choice Models. 7.1
Introduction. 7.2 Binary Choice Models with a Numerical Variable. 7.3
Heterogeneous Binary Choice Models. 7.4 Homogeneous Binary Choice Models.
7.5 General Binary Choice Models. 7.6 Advanced Binary Choice Models. 7.7
Multidimensional Binary Choice Translog Linear Model. 7.8 Piecewise Binary
Choice Models. 7.9 Ordered Choice Models with Numerical Independent
Variables. 7.10 Studies Using General Choice Models. 7.11 Two-Stage Binary
Choice Model. 8 Experimental Data Analysis. 8.1 Introduction. 8.2 Analysis
Based on Cell-Mean Models. 8.3 Bivariate Correlation Analysis. 8.4 Effects
of the Experimental Factors. 8.5 Effects of the Experimental Factors and
Covariates. 8.6 Application of the Ordered Choice Models. 8.7 Application
of Seemingly Causal Models. 8.8 Multivariate Analysis of Covariance. 8.9
Tests for Equality of Medians. 8.10 The Simplest Experimental Design. 9
Seemingly Causal Models Based on Numerical Variables. 9.1 Introduction. 9.2
The Simplest Seemingly Causal Model. 9.3 General Linear Models Based on
Bivariate (X, Y). 9.4 Models Based on Numerical Trivariate. 9.5 Regression
Analysis Using the Principal Components. 9.6 Seemingly Causal Models Based
on (X1, X2, Y1, Y2). 9.7 Seemingly Causal Models Based on (X1, X2, X3, Y1,
Y2). 9.8 New Types of Interaction Model. 9.9 Special Cases. 9.10 Special
Notes and Comments. Appendix A.9.1 Hypothetical Data Set. 10 Factor
Analysis and Latent Variables Models. 10.1 Introduction. 10.2 The Basic
Concept of Factor Analysis. 10.3 The First-Level Latent Variables. 10.4
Illustrations Based on Hamsal's (2006) Data Set. 10.5 Selected Cases Based
on Ary Suta's (2006) Data Set. 10.6 Evaluation Analysis Based on Latent
Variables. 11 Application of the Stepwise Selection Methods. 11.1
Introduction. 11.2 The Options for the Stepwise Selection Methods. 11.3
Selection Method for the Numerical Variable Regression Models. 11.4
Multifactorial Stepwise Regression Models. 11.5 Illustrative Stepwise
Regressions Based on Mlogit.wf1. 11.6 Special Notes and Comments. 12
Censored Multiple Regression Models. 12.1 Introduction. 12.2 Tobit Models.
12.3 General Tobit Model. 12.4 Zero-One Indicator of Censoring. 12.5
Illustrative Cases of Censored Observations. 12.6 Series for a Censoring
Variable. 12.7 Switching Censored Regressions. 12.8 Special Notes and
Comments. Appendix A.12.1 Hypothetical Censored Data, Modified from Faad's
(2008) Data Set. References. Index.