A comprehensive and accessible guide to panel data analysis using EViews softwareThis book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given.Panel Data Analysis using EViews:* Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets.* Examines a variety of panel data models along with the author's own empirical findings, demonstrating the advantages and limitations of each model.* Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data.* Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments.* Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives.* Explores recent new developments in panel data analysisAn essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.

Preface xvAbout the Author xxiPART ONE PANEL DATA AS A MULTIVARIATE TIME SERIES BY STATES 11 Data Analysis Based on a Single Time Series by States 31.1 Introduction 31.2 Multivariate Growth Models 31.3 Alternative Multivariate Growth Models 101.4 Various Models Based on Correlated States 141.5 Seemingly Causal Models with Time-Related Effects 211.6 The Application of the Object POOL 231.7 Growth Models of Sample Statistics 291.8 Special Notes on Time-State Observations 321.9 Growth Models with an Environmental Variable 321.10 Models with an Environmental Multivariate 401.11 Special Piece-Wise Models 492 Data Analysis Based on Bivariate Time Series by States 552.1 Introduction 552.2 Models Based on Independent States 562.3 Time-Series Models Based on Two Correlated States 602.4 Time-Series Models Based on Multiple Correlated States 722.5 Time-Series Models with an Environmental Variable Zt, Based on Independent States 782.6 Models Based on Correlated States 822.7 Piece-Wise Time-Series Models 863 Data Analysis Based on Multivariate Time Series by States 873.1 Introduction 873.2 Models Based on (X_i,Y_i,Z_i) for Independent States 883.3 Models Based on (X_i, Y_i,Z_i) for Correlated States 903.4 Simultaneous SCMs with Trend 963.5 Models Based on (X1_i,X2_i,X3_i, Y1_i,Y2_i) for Independent States 1003.6 Models Based on (X_i,Y_i) for Correlated States 1033.7 Discontinuous Time-Series Models 1063.8 Additional Examples for Correlated States 1073.9 Special Notes and Comments 1094 Applications of Seemingly Causal Models 1114.1 Introduction 1114.2 SCMs Based on a Single Time Series Y_it 1124.3 SCMs Based on Bivariate Time Series (X_it,Y_it) 1184.4 SCMs Based on a Trivariate (X1_i,X2_i,Y1_i) 1204.5 SCMs Based on a Trivariate (X_it,Y1_it,Y2_it) 1264.6 SCMs Based on Multivariate Endogenous and Exogenous Variables 1274.7 Fixed- and Random Effects Models 1334.8 Models with Cross-Section Specific Coefficients 1384.9 Cases in Industry 146PART TWO POOL PANEL DATA ANALYSIS 1495 Evaluation Analysis 1515.1 Introduction 1515.2 Preliminary Evaluation Analysis 1525.3 The Application of the Object "Descriptive Statistics and Tests" 1535.4 Analysis Based on Ordinal Problem Indicators 1585.5 Multiple Association between Categorical Variables 1616 General Choice Models 1656.1 Introduction 1656.2 Multi-Factorial Binary Choice Models 1656.3 Binary Logit Model of Yit on a Numerical Variable Xit 1756.4 Binary Logit Model of a Zero-One Indicator Yit on (X1it,X2it) 1826.5 Binary Choice Model of a Zero-One Indicator Yit on (X1it,X2it,X3it) 1876.6 Binary Choice Model of a Zero-One Indicator Yit on (X1it,. . ., Xhit,. . .) 1906.7 Special Notes and Comments 1907 Advanced General Choice Models 1927.1 Introduction 1927.2 Categorical Data Analyses 1937.3 Multi-Factorial Choice Models with a Numerical Independent Variable 2078 Univariate General Linear Models 2168.1 Introduction 2168.2 ANOVA and Quantile Models 2168.3 Continuous Linear-Effect Models 2218.4 Piece-Wise Autoregressive Linear Models by Time Points 2278.5 ANCOVA Models 2419 Fixed-Effects Models and Alternatives 2449.1 Introduction 2449.2 Cross-Section Fixed-Effects Models 2459.3 Time-Fixed-Effects Models 2519.4 Two-Way Fixed-Effects Models 2549.5 Extended Fixed-Effects Models 2659.6 Selected Fixed-Effects Models from the Journal of Finance, 2011 2749.7 Heterogeneous Regression Models 27810 Special Notes on Selected Problems 28610.1 Introduction 28610.2 Problems with Dummy Variables 28610.3 Problems with the Numerical Variable Rit 28810.4 Problems with the First Difference Variable 29410.5 Problems with Ratio Variables 29510.6 The CAPM and its Extensions or Modifications 29810.7 Selected Heterogeneous Regressions from International Journals 30510.8 Models without the Time-Independent Variable 30810.9 Models with Time Dummy Variables 31110.10 Final Remarks 31211 Seemingly Causal Models 31411.1 Introduction 31411.2 MANOVA Models 31411.3 Multivariate Heterogeneous Regressions by Group and Time 31511.4 MANCOVA Models 31811.5 Discontinuous and Continuous MGLM by Time 31911.6 Illustrative Linear-Effect Models by Times 31911.7 Illustrative SCMs by Group and Time 331PART THREE BALANCED PANEL DATA AS NATURALEXPERIMENTAL DATA 33712 Univariate Lagged Variables Autoregressive Models 33912.1 Introduction 33912.2 Developing Special Balanced Pool Data 33912.3 Natural Experimental Data Analysis 34112.4 The Simplest Heterogeneous Regressions 34312.5 LVAR(1,1) Heterogeneous Regressions 34412.6 Manual Stepwise Selection for General Linear LV(1) Model 36212.7 Manual Stepwise Selection for Binary Choice LV(1) Models 36912.8 Manual Stepwise Selection for Ordered Choice Models 37812.9 Bounded Models by Group and Time 38713 Multivariate Lagged Variables Autoregressive Models 39613.1 Introduction 39613.2 Seemingly Causal Models 39613.3 Alternative Data Analyses 40013.4 SCMs Based on (Y1,Y2) 40113.5 Advanced Autoregressive SCMs 42113.6 SCMs Based on (Y1,Y2) with Exogenous Variables 43014 Applications of GLS Regressions 44114.1 Introduction 44114.2 Cross-Section Random Effects Models (CSREMs) 44114.3 LV(1) CSREMs by Group or Time 44314.4 CSREMs with the Numerical Time Variable 44814.5 CSREMs by Time or Time Period 45414.6 Period Random Effects Models (PEREMs) 46314.7 Illustrative Panel Data Analysis Based on CES.wf1 46514.8 Two-Way Effects Models 46814.9 Testing Hypotheses 47314.10 Generalized Method of Moments/Dynamic Panel Data 48214.11 More Advanced Interaction Effects Models 489References 501Index 509