Cross Section and Experimental Data Analysis Using EViews

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.

Table of Contents:
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.