面板数据分析：英文版

面板数据分析：英文版
作　者： Cheng Hsiao
出版社： 北京大学出版社
丛编项： 经济学前沿影印丛书
版权说明： 本书为公共版权或经版权方授权，请支持正版图书
标　签： 暂缺
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作者简介

　　萧政，是南加州大学经济学教授。本书的第1版已经成为经济学文献中有关面板数据的标准介绍。他还是《经济计量模型、技术与应用》一书的合作者，并与他人共同主编了《面板模型和受限因变量模型分析》及《非线性统计推断》等著作。他是计量经济学会的成员以及《计量经济学杂志》的主编和会员。

内容简介

《面板数据分析》（第2版）是面板数据分析这一领域的经典之作，《面板数据分析》（第2版）系统地介绍了有关面板数据的基本理论，尤其是对面板数据在控制未观察到的个体或时间偏差，以避免设定误差，改善估计效率方面的应用；并且，《面板数据分析》（第2版）审慎地使用了实证研究的案例，这使得《面板数据分析》（第2版）对经济学、商学、社会学和政治科学的研究生和研究人员非常有用。在1986年第一版的成功基础上，《面板数据分析》（第2版）第二版对第一版进行了丰富的修改，以一种严谨易读的方式将有关面板数据研究的近期进展加入到书中，并且使这部分内容与原有的内容融为一体。第二版特别的修改包括：介绍了贝叶斯方法以及在广义矩方法框架下的估计量的严格外生性的概念，使得各种模型的识别联系起来；对估计离散选择模型的半参数方法提出了直觉解释；以及介绍了面板样本选择模型的估计的成对整理方法（methods of pairwise trimming）等。

图书目录

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