Cross Section and Experimental Data Analysis Using EViews

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A practical guide to selecting and applying the most appropriatemodel for analysis of cross section data using EViews. "This book is a reflection of the vast experience andknowledge of the author. It is a useful reference for students andpractitioners dealing with cross sectional data analysis ... Thestrength of the book lies in its wealth of material and wellstructured guidelines ..." Prof. Yohanes EkoRiyanto, Nanyang Technological University, Singapore "This is superb and brilliant. Prof. Agung has skilfullytransformed his best experiences into new knowledge ... creating anew way of understanding data analysis." Dr. I Putu Gede ArySuta, The Ary Suta Center, Jakarta Basic theoretical concepts of statistics as well as samplingmethods are often misinterpreted by students and less experiencedresearchers. This book addresses this issue by providing a hands-onpractical guide to conducting data analysis using EViews combinedwith a variety of illustrative models (and their extensions).Models having numerically dependent variables based on across-section data set (such as univariate, multivariate andnonlinear models as well as non-parametric regressions) areconcentrated on. It is shown that a wide variety of hypotheses caneasily be tested using EViews. Cross Section and Experimental Data Analysis UsingEViews: * Provides step-by-step directions on how to apply EViews tocross section data analysis - from multivariate analysis andnonlinear models to non-parametric regression * Presents a method to test for all possible hypotheses based oneach model * Proposes a new method for data analysis based on amultifactorial design model * Demonstrates that statistical summaries in the form oftabulations are invaluable inputs for strategic decisionmaking * Contains 200 examples with special notes and comments based onthe author's own empirical findings as well as over 400illustrative outputs of regressions from EViews * Techniques are illustrated through practical examples from realsituations * Comes with supplementary material, including work-filescontaining selected equation and system specifications that havebeen applied in the book This user-friendly introduction to EViews is ideal for Advancedundergraduate and graduate students taking finance, econometrics,population, or public policy courses, as well as applied policyresearchers.


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.

"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

This book offers an easily accessible, practical approach to the analysis of cross section and experimental data using the versatile Windows-based computer software program EViews. With a wealth of examples and an emphasis on defining and testing statistical hypotheses, Cross Section and Experimental Data Analysis Using EViews is an essential resource for solving real-life problems.

The author:

  • Supplies step-by-step directions on application of EViews to cross section data analysis - from multivariate analysis and nonlinear models to non-parametric regression
  • Proffers guidance on developing relevant descriptive statistical summaries based on evaluation studies
  • Explains how to develop and evaluate statistical empirical models without the need for computational formulae
  • Discusses alternative methods for data analysis based on various experimental data sets
  • Presents over 200 examples as well as special notes and comments derived from his own experience, and illustrates techniques through practical examples from real situations

Designed as an instructive guide, Cross Section and Experimental Data Analysis Using EViews is ideally suited to undergraduate and graduate students taking econometrics, population, or public policy courses. Applied policy researchers, decision makers, and bureaucrats will also find this book indispensible.

Access supplementary material for this text at the companion website:


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 ConcordanceDiscordance 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.

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Cross Section and Experimental Data Analysis Using EViews
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