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.
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Yes, you can access Panel Data Analysis using EViews by I. Gusti Ngurah Agung in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Part One Panel Data as a Multivariate Time Series by States
Abstract
Part I, containing the first four chapters, considers unstacked panel data where the units of the analysis are time observations. So the sets or multidimensional exogenous, endogenous and environmental variables, respectively, for the state i can be presented using the symbols;
,
, and
, for
; and
. Note that the scores of the environmental variables are constant for all states or individuals. Using these symbols, panel data is considered as the data of multivariate time series by states (countries, regions, agencies, firms, industries, households or individuals).
It is noted that all of the time series models presented in Agung (2009a) can easily be applied to conduct the data analysis based on each state in the panel data; as well as the general multivariate models by states and time periods, presented in Section 3.7. This part presents just the specific analyses for unstacked data with a small number of N . The models for a large N will be presented in Part II and Part III.
1 Data Analysis Based on a Single Time Series by States
1.1 Introduction
Panel data can be viewed as a finite set of time-series data. As an illustration Table 1.1 presents part of the data in POOLG7.wf1, namely unstacked data, consisting of a single time series GDP from seven countries. Note that this table shows seven time series variables, namely GDP_CANt to GDP_USt.
Table 1.1A subset of the unstacked data in POOLG7.wf1
Based on each time series of GDP by states, various growth models can be considered as presented in Agung (2009a, Chapter 2), starting with classical growth models, namely geometric and exponential growth models, and their extensions. Therefore, based on the seven states, the multivariate growth models should be applied as presented in the following sections.
1.2 Multivariate Growth Models
1.2.1 Continuous Growth Models
In general, let Yit be the observed value of the variable Y for the i -th individual (a country, state, region, agency, community, household or person) at time t, for i = 1,…,N, and t = 1,…,T. I...
Table of contents
Cover
Title Page
Copyright
Dedication
Preface
About the Author
Part One: Panel Data as a Multivariate Time Series by States
Part Two: Pool Panel Data Analysis
Part Three: Balanced Panel Data as Natural Experimental Data
