Panel data analysis using eviews

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Title:

Author:

Agung, I. Gusti Ngurah.

ISBN:

9781118715581

Personal Author:

Agung, I. Gusti Ngurah.

Physical Description:

xx, 517 pages : illustrations ; 25 cm

Contents:

PART ONE PANEL DATA AS A MULTIVARIATE TIME SERIES BY STATES -- 1 Data Analysis Based on a Single Time Series by States -- 1.1 Introduction -- 1.2 Multivariate Growth Models -- 1.3 Alternative Multivariate Growth Models -- 1.4 Various Models Based on Correlated States -- 1.5 Seemingly Causal Models with Time-Related Effects -- 1.6 The Application of the Object POOL -- 1.7 Growth Models of Sample Statistics -- 1.8 Special Notes on Time-State Observations -- 1.9 Growth Models with an Environmental Variable.

1.10 Models with an Environmental Multivariate -- 1.11 Special Piece-Wise Models -- 2 Data Analysis Based on Bivariate Time Series by States -- 2.1 Introduction -- 2.2 Models Based on Independent States -- 2.3 Time-Series Models Based on Two Correlated States -- 2.4 Time-Series Models Based on Multiple Correlated States -- 2.5 Time-Series Models with an Environmental Variable Zt, Based on Independent States -- 2.6 Models Based on Correlated States -- 2.7 Piece-Wise Time-Series Models -- 3 Data Analysis Based on Multivariate Time Series by States.

3.1 Introduction -- 3.2 Models Based on (X_i, Y_i, Z_i) for Independent States -- 3.3 Models Based on (X_i, Y_i, Z_i) for Correlated States -- 3.4 Simultaneous SCMs with Trend -- 3.5 Models Based on (X1_i, X2_i, X3_i, Y1_i, Y2_i) for Independent States -- 3.6 Models Based on (X_i, Y_i) for Correlated States -- 3.7 Discontinuous Time-Series Models -- 3.8 Additional Examples for Correlated States -- 3.9 Special Notes and Comments -- 4 Applications of Seemingly Causal Models.

4.1 Introduction -- 4.2 SCMs Based on a Single Time Series Y_it -- 4.3 SCMs Based on Bivariate Time Series (X_it, Y_it) -- 4.4 SCMs Based on a Trivariate (X1_i, X2_i, Y1_i) -- 4.5 SCMs Based on a Trivariate (X_it, Y1_it, Y2_it) -- 4.6 SCMs Based on Multivariate Endogenous and Exogenous Variables -- 4.7 Fixed- and Random Effects Models -- 4.8 Models with Cross-Section Specific Coefficients -- 4.9 Cases in Industry -- PART TWO POOL PANEL DATA ANALYSIS -- 5 Evaluation Analysis -- 5.1 Introduction -- 5.2 Preliminary Evaluation Analysis.

6.7 Special Notes and Comments -- 7 Advanced General Choice Models -- 7.1 Introduction -- 7.2 Categorical Data Analyses -- 7.3 Multi-Factorial Choice Models with a Numerical Independent Variable -- 8 Univariate General Linear Models -- 8.1 Introduction -- 8.2 ANOVA and Quantile Models -- 8.3 Continuous Linear-Effect Models -- 8.4 Piece-Wise Autoregressive Linear Models by Time Points -- 8.5 ANCOVA Models -- 9 Fixed-Effects Models and Alternatives.

9.1 Introduction -- 9.2 Cross-Section Fixed-Effects Models -- 9.3 Time-Fixed-Effects Models -- 9.4 Two-Way Fixed-Effects Models -- 9.5 Extended Fixed-Effects Models -- 9.6 Selected Fixed-Effects Models from the Journal of Finance, 2011 -- 9.7 Heterogeneous Regression Models -- 10 Special Notes on Selected Problems -- 10.1 Introduction -- 10.2 Problems with Dummy Variables -- 10.3 Problems with the Numerical Variable Rit -- 10.4 Problems with the First Difference Variable.

10.5 Problems with Ratio Variables -- 10.6 The CAPM and its Extensions or Modifications -- 10.7 Selected Heterogeneous Regressions from International Journals -- 10.8 Models without the Time-Independent Variable -- 10.9 Models with Time Dummy Variables -- 10.10 Final Remarks -- 11 Seemingly Causal Models -- 11.1 Introduction -- 11.2 MANOVA Models -- 11.3 Multivariate Heterogeneous Regressions by Group and Time -- 11.4 MANCOVA Models -- 11.5 Discontinuous and Continuous MGLM by Time -- 11.6 Illustrative Linear-Effect Models by Times -- 11.7 Illustrative SCMs by Group and Time.

PART THREE BALANCED PANEL DATA AS NATURAL EXPERIMENTAL DATA -- 12 Univariate Lagged Variables Autoregressive Models -- 12.1 Introduction -- 12.2 Developing Special Balanced Pool Data -- 12.3 Natural Experimental Data Analysis -- 12.4 The Simplest Heterogeneous Regressions -- 12.5 LVAR(1,1) Heterogeneous Regressions -- 12.6 Manual Stepwise Selection for General Linear LV(1) Model -- 12.7 Manual Stepwise Selection for Binary Choice LV(1) Models -- 12.8 Manual Stepwise Selection for Ordered Choice Models -- 12.9 Bounded Models by Group and Time.

13 Multivariate Lagged Variables Autoregressive Models -- 13.1 Introduction -- 13.2 Seemingly Causal Models -- 13.3 Alternative Data Analyses -- 13.4 SCMs Based on (Y1,Y2) -- 13.5 Advanced Autoregressive SCMs -- 13.6 SCMs Based on (Y1,Y2) with Exogenous Variables -- 14 Applications of GLS Regressions -- 14.1 Introduction -- 14.2 Cross-Section Random Effects Models (CSREMs) -- 14.3 LV(1) CSREMs by Group or Time -- 14.4 CSREMs with the Numerical Time Variable -- 14.5 CSREMs by Time or Time Period -- 14.6 Period Random Effects Models (PEREMs) -- 14.7 Illustrative Panel Data Analysis Based on CES.wf1.

14.8 Two-Way Effects Models -- 14.9 Testing Hypotheses -- 14.10 Generalized Method of Moments/Dynamic Panel Data -- 14.11 More Advanced Interaction Effects Models.

Abstract:

"Panel Data Analysis using EViews provides graduate students, researchers, and statisticians with step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. The author explores a variety of panel data models, along with the authors own empirical findings, demonstrating the advantages and limitations of each model. The text also examines various alternative models based on panel data, as well as the best and worst ANCOVA models"-- Provided by publisher.

"This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets"-- Provided by publisher.

Title Subject:

EViews (Computer file)

Subject Term:

Statistics.

Copies:

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Summary

A comprehensive and accessible guide to panel data analysis using EViews software

This 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 analysis

An 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.