面板數(shù)據(jù)分析：英文版

定　價：￥48.00

作　者：	（美）Cheng Hsiao著
出版社：	北京大學出版社
叢編項：	經(jīng)濟學前沿影印叢書
標　簽：	暫缺

購買這本書可以去

ISBN：	9787301092187	出版時間：	2005-09-01	包裝：	平裝
開本：	24cm	頁數(shù)：	366	字數(shù)：

內(nèi)容簡介

　　《面板數(shù)據(jù)分析》（第2版）是面板數(shù)據(jù)分析這一領域的經(jīng)典之作，《面板數(shù)據(jù)分析》（第2版）系統(tǒng)地介紹了有關面板數(shù)據(jù)的基本理論，尤其是對面板數(shù)據(jù)在控制未觀察到的個體或時間偏差，以避免設定誤差，改善估計效率方面的應用；并且，《面板數(shù)據(jù)分析》（第2版）審慎地使用了實證研究的案例，這使得《面板數(shù)據(jù)分析》（第2版）對經(jīng)濟學、商學、社會學和政治科學的研究生和研究人員非常有用。在1986年第一版的成功基礎上，《面板數(shù)據(jù)分析》（第2版）第二版對第一版進行了豐富的修改，以一種嚴謹易讀的方式將有關面板數(shù)據(jù)研究的近期進展加入到書中，并且使這部分內(nèi)容與原有的內(nèi)容融為一體。第二版特別的修改包括：介紹了貝葉斯方法以及在廣義矩方法框架下的估計量的嚴格外生性的概念，使得各種模型的識別聯(lián)系起來；對估計離散選擇模型的半?yún)?shù)方法提出了直覺解釋；以及介紹了面板樣本選擇模型的估計的成對整理方法（methods of pairwise trimming）等。

作者簡介

　　蕭政，是南加州大學經(jīng)濟學教授。本書的第1版已經(jīng)成為經(jīng)濟學文獻中有關面板數(shù)據(jù)的標準介紹。他還是《經(jīng)濟計量模型、技術與應用》一書的合作者，并與他人共同主編了《面板模型和受限因變量模型分析》及《非線性統(tǒng)計推斷》等著作。他是計量經(jīng)濟學會的成員以及《計量經(jīng)濟學雜志》的主編和會員。

圖書目錄

PrefacetotheSecondEdition
PrefacetotheFirstEdition
Chapter1.Introduction
1.1AdvantagesofPanelData
1.2IssuesInvolvedinUtilizingPanelData
1.2.1HeterogeneityBias
1.2.2SelectivityBias
1.3OutlineoftheMonograph
Chapter2.AnalysisofCovariance
2.1Introduction
2.2AnalysisofCovariance
2.3AnExample
Chapter3.SimpleRegressionwithVariableIntercepts
3.1Introduction
3.2Fixed-EffectsModels:Least-SquaresDummy-VariableApproach
3.3Random-EffectsModels:EstimationofVariance-ComponentsModels
3.3.1CovarianceEstimation
3.3.2Generalized-Least-SquaresEstimation
3.3.3MaximumLikelihoodEstimation
3.4FixedEffectsorRandomEffects
3.4.1AnExample
3.4.2ConditionalInferenceorUnconditional(Marginal)Inference
3.4.2.aMundlak'sFormulation
3.4.2.bConditionalandUnconditionalInferencesinthePresenceorAbsenceofCorrelationbetweenIndividualEffectsandAttributes
3.5TestsforMisspecification
3.6ModelswithSpecificVariablesandBothIndividual-andTime-SpecificEffects
3.6.1EstimationofModelswithIndividual-SpecificVariables
3.6.2EstimationofModelswithBothIndividualandTimeEffects
3.7Heteroscedasticity
3.8ModelswithSeriallyCorrelatedErrors
3.9ModelswithArbitraryErrorStructure-ChamberlainπApproach
Appendix3A:ConsistencyandAsymptoticNormalityofthe
Minimum-DistanceEstimator
Appendix3B:CharacteristicVectorsandtheInverseofthe
Variance-CovarianceMatrixofa
Three-ComponentModel
Chapter4.DynamicModelswithVariableIntercepts
4.1Introduction
4.2TheCovarianceEstimator
4.3Random-EffectsModels
4.3.1BiasintheOLSEstimator
4.3.2ModelFormulation
4.3.3EstimationofRandom-EffectsModels
4.3.3.aMaximumLikelihoodEstimator
4.3.3.bGeneralized-Least-SquaresEstimator
4.3.3.cInstrumental-VariableEstimator
4.3.3.dGEneralizedMethodofMomentsEstimator
4.3.4TestingSomeMaintainedHypothesesonInitialConditions
4.3.5SimulationEvidence
4.4AnExample
4.5Fixed-EffectsModels
4.5.1TransformedLikelihoodApproach
4.5.2Minimum-DistanceEstimator
4.5.3RelationsbetweentheLikelihood-BasedEstimatorandtheGeneralizedMethodofMomentsEstimator(GMM)
4.5.4Random-versusFixed-EffectsSpecification
4.6EstimationofDynamicModelswithArbitaryCorrelationsintheResiduals
4.7Fixed-EffectsVectorAutoregressiveModels
4.7.1ModelFormulation
4.7.2GeneralizedMethodofMoments(GMM)Estimation
4.7.3(Transformed)MaximumLikelihoodEstimator
4.7.4Minimum-DistanceEstimator
Appendix4A:DerivationoftheAsymptoticCovarianceMatrixoftheFeasibleIVIDE
Chapter5.Simultaneous-EquationsModels
5.1Introduction
5.2JointGeneralized-Least-SquaresEstimationTechnique
5.3EstimationofStructuralEquations
5.3.1EstimationofaSingleEquationintheStructuralModel
5.3.2EstimationoftheCompleteStructuralSystem
5.4TriangularSystem
5.4.1Identification
5.4.2Estimation
5.4.2.aInstrumental-VariableMethod
5.4.2.bMaximum-LikelihoodMethod
5.4.3AnExample
Appendix5A
Chapter6.Variable-CoefficientModels
6.1Introduction
6.2CoefficientsThatVaryoverCross-SectionalUnits
6.2.1Fixed-CoefficientModel
6.2.2Random-CoefficientModel
6.2.2.aTheModel
6.2.2.bEstimation
6.2.2.cPredictingIndividualCoefficients
6.2.2.dTestingforCoefficientVariation
6.2.2.eFixedorRandomCoefficients
6.2.2.fAnExample
6.3CoefficientsThatVaryoverTimeandCross-SectionalUnits
6.3.1TheModel
6.3.2Fixed-CoefficientModel
6.3.3Random-CoefficientModel
6.4CoefficientsThatEvolveoverTime
6.4.1TheModel
6.4.2PredictingβtbytheKalmanFilter
6.4.3MaximumLikelihoodEstimation
6.4.4TestsforParameterConstancy
6.5CoefficientsThatAreFunctionsofOtherExogenousVariables
6.6AMixedFixed-andRandom-CoefficientsModel
6.6.1ModelFormulation
6.6.2ABayesSolution
6.6.3AnExample
6.6.4RandomorFixedParameters
6.6.4.aAnExample
6.6.4.bModelSelection
6.7Dy.namicRandom-CoefficientModels
6.8AnExample-LiquidityConstraintsandFirmInvestmentExpenditure
Appendix6A:CombinationofTwoNormalDistributions
Chapter7.DiscreteData
7.1Introduction
7.2SomeDiscrete-ResponseModels
7.3ParametricApproachtoStaticModelswithHeterogeneity
7.3.1Fixed-EffectsModels
7.3.1.aMaximumLikelihoodEstimator
7.3.1.bConditionsfortheExistenceofaConsistentEstimator
7.3.1.cSomeMonteCarloEvidence
7.3.2Random-EffectsModels
7.4SemiparametricApproachtoStaticModels
7.4.1MaximumScoreEstimator
7.4.2ARoot-NConsistentSemiparametricEstimator
7.5DynamicModels
7.5.1TheGeneralModel
7.5.2InitialConditions
7.5.3AConditionalApproach
7.5.4StateDependenceversusHeterogeneity
7.5.5TwoExamples
7.5.5.aFemaleEmployment
7.5.5.bHouSeholdBrandChoices
Chapter8.TruncatedandCensoredData
8.1Introduction
8.2AnExample-NonrandomlyMissingData
8.2.1Introduction
8.2.2AProbabilityModelofAttritionandSelectionBias
8.2.3AttritionintheGaryIncome-MaintenanceExperiment
8.3TobitModelswithRandomIndividualEffects
8.4Fixed-EffectsEstimator
8.4.1PairwiseTrimmedLeast-Squaresand
Least-Absolute-DeviationEstimatorsfor
TruncatedandCensoredRegressions
8.4.1.aTruncatedRegression
8.4.1.bCensoredRegressions
8.4.2ASemiparametricTwo-StepEstimatorfortheEndogenouslyDeterminedSampleSelectionModel
8.5AnExample:HousingExpenditure
8.6DynamicTobitModels
8.6.1DynamicCensoredModels
8.6.2DynamicSampleSelectionModels
Chapter9.IncompletePanelData
9.1EstimatingDistributedLagsinShortPanels
9.1.1Introduction
9.1.2CommonAssumptions
9.1.3IdentificationUsingPriorStructureoftheProcessoftheExogenousVariable
9.1.4IdentificationUsingPriorStructureoftheLagCoefficients
9.1.5EstimationandTesting
9.2RotatingorRandomlyMissingData
9.3Pseudopanels(orRepeatedCross-SectionalData)
9.4PoolingofaSingleCross-SectionalandaSingleTime-SeriesDataSet
9.4.1Introduction
9.4.2TheLikelihoodApproachtoPoolingCross-SectionalandTime-SeriesData
9.4.3AnExample
Chapter10.MiscellaneousTopics
10.1SimulationMethods
10.2PanelswithLargeNandT
10.3Unit-RootTests
10.4DatawithMultilevelStructures
10.5ErrorsofMeasurement
10.6ModelingCross-SectionalDependence
Chapter11.ASummaryView
11.1Introduction
11.2BenefitsandLimitationsofPanelData
11.2.1IncreasingDegreesofFreedomandLesseningtheProblemofMulticollinearity
11.2.2IdentificationandDiscriminationbetweenCompetingHypotheses
11.2.3ReducingEstimationBias
11.2.3.aOmitted-VariableBias
11.2.3.bBiasInducedbytheDynamicStructureofaModel
11.2.3.cSimultaneityBias
11.2.3.dBiasInducedbyMeasurementErrors
11.2.4ProvidingMicroFoundationsforAggregateDataAnalysis
11.3EfficiencyoftheEstimates
Notes
References
AuthorIndex
SubjectIndex