Panel Data Analysis using EViews

541 Pages·2013·58.35 MB·English

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Panel Data Analysis
Using EViews
Panel Data Analysis
P
a
n
I Gusti Ngurah Agung e
Using EViews
l
Graduate School of Management, Faculty of Economics and Business, University of Indonesia
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A comprehensive and accessible guide to panel data analysis using EViews software a
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This book explores the use of EViews software in creating panel data analysis using appropriate
a
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
A
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. n
a
Panel Data Analysis Using EViews:
l
provides step-by-step guidance on how to apply eViews software to panel data analysis y
using appropriate empirical models and real datasets. s
i
examines a variety of panel data models along with the author’s own empirical s
findings, demonstrating the advantages and limitations of each model.
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presents growth models, time-related effects models, and polynomial models,
in addition to models commonly applied to panel data.
s
includes more than 250 examples divided into three groups of models (stacked, i
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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 g
on viable alternatives.

explores recent new developments in panel data analysis. E
V
An essential tool for advanced undergraduate or graduate students and applied researchers in the
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fields of finance, econometrics and population studies. Statisticians and data analysts involved with e
data collected over long time periods will also find this book a useful resource.
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I Gusti Ngurah Agung
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http://www.wiley.com/go/panel_data

Panel Data Analysis Using EViews

Panel Data Analysis Using EViews
IGustiNgurahAgung
GraduateSchoolofManagement
FacultyofEconomicsandBusiness,UniversityofIndonesia
Thiseditionfirstpublished2014
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LibraryofCongressCataloging-in-PublicationData
Agung,IGustiNgurah.
Paneldataanalysisusingeviews/I.GustiNgurahAgung.
pagescm
Includesbibliographicalreferencesandindex.
ISBN978-1-118-71558-1(hardback)
1. Statistics. 2. EViews(Computerfile) I. Title.
HA29.A3772014
005.5’5–dc23
2013024207
AcataloguerecordforthisbookisavailablefromtheBritishLibrary.
ISBN: 978-1-118-71558-1
Setin10/12ptTimesbyThomsonDigital,Noida,India.
1 2014
Dedicated to my wife
Anak Agung Alit Mas,
as well as all my Generation

Contents
Preface xv
AbouttheAuthor xxi
PARTONE PANELDATAASAMULTIVARIATETIMESERIESBYSTATES 1
1 DataAnalysisBasedonaSingleTimeSeriesbyStates 3
1.1 Introduction 3
1.2 MultivariateGrowthModels 3
1.2.1 ContinuousGrowthModels 3
1.2.2 DiscontinuousGrowthModels 8
1.3 AlternativeMultivariateGrowthModels 10
1.3.1 AGeneralizationofMAR(p)_GM 10
1.3.2 MultivariateLaggedVariablesGrowthModels 11
1.3.3 MultivariateLagged-VariableAutoregressiveGrowthModels 13
1.3.4 BoundedMLVAR(p;q)_GM 13
1.3.5 SpecialNotes 13
1.4 VariousModelsBasedonCorrelatedStates 14
1.4.1 SeeminglyCausalModelswithTrend 15
1.4.2 TheApplicationoftheObject“VAR” 18
1.4.3 TheApplicationoftheInstrumentalVariablesModels 20
1.5 SeeminglyCausalModelswithTime-RelatedEffects 21
1.5.1 SCMBasedonthePathDiagraminFigure1.10(a) 21
1.5.2 SCMBasedonthePathDiagraminFigure1.10(b) 22
1.6 TheApplicationoftheObjectPOOL 23
1.6.1 WhatisaFixed-EffectModel? 23
1.6.2 WhatisaRandomEffectModel? 25
1.6.3 SpecialNotes 27
1.7 GrowthModelsofSampleStatistics 29
1.8 SpecialNotesonTime-StateObservations 32
1.9 GrowthModelswithanEnvironmentalVariable 32
1.9.1 TheSimplestPossibleModel 33
1.9.2 TheApplicationofVARandVECModels 34
1.9.3 ApplicationofARCHModel 37
1.9.4 TheApplicationofInstrumentalVariablesModels 38
viii Contents
1.10 ModelswithanEnvironmentalMultivariate 40
1.10.1 BivariateCorrelationandSimpleLinearRegressions 40
1.10.2 SimpleModelswithanEnvironmentalMultivariate 42
1.10.3 TheVARModels 43
1.11 SpecialPiece-WiseModels 49
1.11.1 TheApplicationofGrowthModels 49
1.11.2 EqualityTestsbyClassifications 53
2 DataAnalysisBasedonBivariateTimeSeriesbyStates 55
2.1 Introduction 55
2.2 ModelsBasedonIndependentStates 56
2.2.1 MAR(p)GrowthModelwithanExogenousVariable 56
2.2.2 AGeneralMAR(p)ModelwithanExogenousVariable 56
2.3 Time-SeriesModelsBasedonTwoCorrelatedStates 60
2.3.1 AnalysisusingtheObjectSystem 61
2.3.2 Two-SLSInstrumentalVariablesModels 65
2.3.3 Three-SLSInstrumentalVariablesModels 69
2.3.4 AnalysisusingtheObject“VAR” 70
2.4 Time-SeriesModelsBasedonMultipleCorrelatedStates 72
2.4.1 ExtensionofthePathDiagraminFigure2.6 72
2.4.2 SCMsasVARModels 74
2.5 Time-SeriesModelswithanEnvironmentalVariableZ Basedon
t,
IndependentStates 78
2.5.1 TheSimplestPossibleModel 78
2.5.2 InteractionModelsBasedonTwoIndependentStates 81
2.6 ModelsBasedonCorrelatedStates 82
2.6.1 MLV(1)InteractionModelwithTrend 83
2.6.2 SimultaneousSCMswithTrend 83
2.7 Piece-WiseTime-SeriesModels 86
3 DataAnalysisBasedonMultivariateTimeSeriesbyStates 87
3.1 Introduction 87
3.2 ModelsBasedon(X_i,Y_i,Z_i)forIndependentStates 88
3.2.1 MLVAR(p;q)ModelwithTrendBasedon(X_i,Y_i,Z_i) 88
3.3 ModelsBasedon(X_i,Y_i,Z_i)forCorrelatedStates 90
3.3.1 MLV(1)InteractionModelwithTrend 91
3.3.2 MLV(1)InteractionModelwithTime-RelatedEffects 93
3.4 SimultaneousSCMswithTrend 96
3.4.1 TheBasicSimultaneousSCMswithTrend 96
3.4.2 AlternativeTimeSeriesModels 98
3.5 ModelsBasedon(X1_i,X2_i,X3_i,Y1_i,Y2_i)forIndependentStates 100
3.5.1 LaggedEndogenousVariables:FirstAutoregressiveModelwith
ExogenousVariablesandTrend 100
3.5.2 AMixedLaggedVariablesFirstAutoregressiveModelwithTrend 101
3.5.3 LaggedEndogenousVariables:FirstAutoregressiveModelwith
ExogenousVariablesandTime-RelatedEffects 102
3.5.4 VariousInteractionModels 103