Panel Data Analysis using EViews (eBook)

I. Gusti Ngurah Agung (Autor)

eBook Download: EPUB

2013
John Wiley & Sons (Verlag)
978-1-118-71556-7 (ISBN)

Infos

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.

I Gusti Ngurah Agung, Graduate School of Management, Faculty of Economics and Business, University of Indonesia

Textauszug

Preface

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 independent variable or time and time-period dummies variables. Note that the numerical time variable has been used to present classical growth models, namely the geometric and exponential growth models (Agung, 2009a, 2011b). Furthermore, the time t in fact represents an environmental variable, which is invariant over individuals or research objects.

The models presented in this book in fact are derived from my first two books in the data analysis using EViews (Agung, 2009a, 2011). For this reason, I recommend to readers to use the models in the first two books as the basic and main references to develop various alternative or more advanced models based on panel data, because this book only presents some of the models.

Furthermore, special statistical results using the object VAR and GLS are illustrated, which have not been presented in other books as well as papers in the international journals. A manual stepwise selection method is introduced, aside from the application of the STEPLS estimation method provide by EViews. Even though STEPLS regressions have been commonly applied, my book proposes and introduces how to apply the STEPLS, using a multistage stepwise selection method specifically for interaction models with numerical and categorical independent variables; such as continuous interaction models by groups of the research objects (firms or individuals) and time points or time periods.

Based on my own point of view, models based on panel data should be classified into three groups; namely (1) The group of models based on unstacked data, or the group of time-series models by states (firms or individuals); (2) The group of models based on stacked or pool data, especially for incomplete panel data; and (3) The group of models based on natural experimental or special structural balanced panel data. For this reason, this book contains 14 chapters, which are classified into three parts.

Part I presents the Time Series Data Analyses by States. In this part the panel data considered is unstacked data, where the units of the analysis are time observations. The sets or multi-dimensionals of exogenous, endogenous and environmental variables, respectively, for the state i can be presented using the symbols X _i t = (X1_i, … , Xk_i, …)t, Y_ i t = (Y1_i, … , Yg _i, …)t, and Z t = (Z1, … , Zj, …)t, for i = 1, … , N; and t = 1, … , T.

Note that the scores of the environmental variables are constant for all states or individuals. All the time series models presented in Agung (2009a) are valid models for each state i. This part presents only the analyses specifically for the unstacked data with a small number of N. The models for a large N will be presented in Part II and Part III.

The four chapters contained in this part are as follows:

Chapter 1 presents multivariate data analyses based on a single time series by states, using various models, multivariate lagged-variable autoregressive growth models, namely MLVAR(p,q)_GM, seemingly causal models (SCMs) with trend or time-related effects, fixed-effects and random effects models, VAR and VEC models. In addition, this chapter also presents piece-wise models, various models having environmental independent variables, TGARCH(a,b,c) and instrumental variables models.

Chapter 2 presents multivariate data analyses based on bivariate time series by states, as the extension of all models presented in Chapter 1. In addition, this chapter also presents simultaneous causal models.

Chapter 3 presents multivariate data analyses based on multivariate time series by states, as the extension of all models presented in Chapter 2. In addition, this chapter also presents special VAR models with an environmental multivariate, which have not been found in other books and papers.

Chapter 4 presents the application of various SCMs, either additive or interaction models, based on a single time series Y_i t, bivariate time series (X_i t ,Y_i t ), trivariate time series (X1_i t ,X2_i t ,Y_i t ) or (X_i t ,Y1_i t ,Y2_i t ), and the application of SCMs as the alternative VAR models with the environmental multivariate presented in Chapter 3.

Part II presents Pool Panel Data Analyses. In this part the panel data considered is stacked data where the units of the analysis are the individual-time or firm-time observations. So the sets or multi-dimensionals of exogenous, endogenous and environmental variables, respectively, for the firm i, at the time t can be presented using the symbols X it = (X1, … , Xk, …)it, Y it = (Y1, … , Yg, …)it, and Z t = (Z1, … ,Zj, …)t, for i = 1, … ,N t ; and t = 1, … ,T. Note that the symbol N t is used to indicate that the models presented in this part should be valid for incomplete or unbalanced pool panel data as well as balanced pool panel data. However, special models for the balanced panel data, as a natural experimental data, will be presented in Part III.

The statistical methods and models applied can directly be derived from the models based on cross-section data presented in...

Produkt-Details

• Erscheint lt. Verlag: 31.12.2013
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Angew. Wahrscheinlichkeitsrechn. u. Statistik / Modelle • Applied Probability & Statistics - Models • Causal Models • cross section independence • Data Analysis • Datenanalyse • dynamic heterogenous estimations • dynamic panel models • Finanz- u. Wirtschaftsstatistik • fixed effect models • growth models • I. Gusti Ngurah Agung • non-stationary panels • Panel Data Analysis using EViews • Polynomial Models • Statistics • Statistics for Finance, Business & Economics • Statistik • time-related effects models • univariate general linear models
• ISBN-10: 1-118-71556-X / 111871556X
• ISBN-13: 978-1-118-71556-7 / 9781118715567