Panel Data Analysis using EViews
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.
I Gusti Ngurah Agung , Graduate School of Management, Faculty of Economics and Business, University of Indonesia
Panel Data Analysis using EViews
The main objectives of this book are to present (1) various general equation of panel data models, with some specific models; (2) various illustrative statistical results based on selected specific models with special notes and comments and (3) comparative studies between sets of special type models, sucha s heterogeneous regression, fixed-effects and random effects models, so that readers can be informed of a model's limitation(s) compared to the others in the set.
This book presents over 250 illustrative examples of panel data analysis using EViews, compared to the books of Baltagi (2009a,b) on Econometric Analysis of Panel Data and A Companion to Econometric Analysis of Panel Data which mainly present the mathematical concepts of the models with some data analysis. Referring to the fixed- and random effects models, Baltagi presented statistical results based on various additive models and none with the numerical time independent variable. However, Baltagi quotes a simple dynamic panel data model with heterogeneous coefficients on the lagged dependent variable and the time trend presented by Wansbeek and Knaap (1999, in Baltagi, 2009a, p. 168), and a random walk model with heterogeneous trend presented by Hardi (2000, in Baltagi, 2009a).
Similarly, this is the case for most of the panel data models presented in Gujarati (2003). Wooldridge (2002), and in more than 300 papers presented in five international journals, such as the Journal of Finance (JOF) from the years 2010 and 2011, International Journal of Accounting (IJA), Journal of Accounting and Economics (JAE), British Accounting Review (BAR), and Advances in Accounting, incorporating Advances in International Accounting (AA) from the years 2008, 2009 and 2010, which are additive models.
However, it is important to note that Wooldridge (2002) presented a random effect model with trend or the numerical time independent variable, Bansal (2005) presented the models with trend and Time-Related Effects (TRE), but based on time series data, and Agung (2009a) presented various models with trend and TRE. So I would say that various models, either additive or interaction models, with the numerical time independent variable or the time and time-period dummy variables, should be acceptable or valid and reliable panel data models.
I found that a very limited number of models with interaction independent variables or heterogeneous regressions models were presented. Only Giroud and Mueller (2011) presented several Year-Industry fixed effects interaction models (or Year-Industry FEMs with interaction independent variables). Referring to the dummy variables models, (Siswantoro and Agung, 2010) presented their findings that only 63 out of 268 papers in the four journals ( IJA , JAE , BAR and AA ), had dummy variables models, and only five of the models had interaction independent variables. In addition, Dharmapala, et al. (2011) presented interaction models or heterogeneous regressions using the Firm and Year dummies, and Park and Jang (2011) presented an interaction period-fixed-effects model with 34 parameters, besides the year dummies. In fact, the heterogeneous regressions model, which is an interaction model, was introduced by Johnson and Neyman in 1962 (cited in Huitema, 1980).
If a multiple regression panel data model does not have any dummy variable, then the regression model presents a single continuous model for whole individual-time observations. I would consider such a model to be inappropriate. On the other hand, a dummy variables model could also be the worst within its group with the same set of numerical and categorical independent variables, which are illustrated in this book.
Referring to various models indicated here, this book presents various models, either additive or interaction models, with the numerical time indep