结构宏观计量经济学（第2版）

图书标准信息

• 作者 [美]德容（David N.DeJong） 著
• 出版社 世界图书出版公司
• 出版时间 2013-03
• 版次 2
• ISBN 9787510058226
• 定价 69.00元
• 装帧 平装
• 开本 24开
• 纸张 胶版纸
• 页数 418页
• 正文语种 英语

【内容简介】

　　《结构宏观计量经济学（第2版）》全面地讲述了形成国民经济整体的各种力量所用到的方法论，模型和技巧。《结构宏观计量经济学（第2版）》强调了时间序列计量方法以及理论和经验研究，并且将这个领域的重大突破也予以考虑。主要内容包括：背景综述；典范型中的铸造模型；DSGE模型的三个模型；模型解技巧：线性解技巧；非线性解技巧；（三）数据表示和准备：去除趋势和孤立循环；当所有变量可观测时的的时间序列行为和；状态空间表示；模特卡罗方法：模特卡罗积分基础；运用序列模特卡罗方法的似然估计和过滤状态空间表示；经验方法：校准；矩匹配；极大似然；贝叶斯方法。

【目录】

Preface
PrefacetotheFirstEdition
PartIIntroduction
1BackgroundandOverview
1.1Background
1.2Overview
2CastingModelsinCanonicalForm
2.1Notation
2.1.1Log-LinearModelRepresentations
2.1.2NonlinearModelRepresentations
2.2Linearization
2.2.1TaylorSeriesApproximation
2.2.2Log-LinearApproximations
2.2.3ExampleEquations
3DSGEModels:ThreeExamples
3.1ModelI:ARealBusinessCycleModel
3.1.1Environment
3.1.2TheNonlinearSystem
3.1.3Log-Linearization
3.2ModelII:MonopolisticCompetitionandMonetaryPolicy
3.2.1Environment
3.2.2TheNonlinearSystem
3.2.3Log-Linearization
3.3ModelIII:AssetPricing
3.3.1Single-AssetEnvironment
3.3.2Multi-AssetEnvironment
3.3.3AlternativePreferenceSpecifications

PartIIModelSolutionTechniques
4LinearSolutionTechniques
4.1HomogeneousSystems
4.2ExampleModels
4.2.1TheOptimalConsumptionModel
4.2.2AssetPricingwithLinearUtility
4.2.3Ramsey'sOptimalGrowthModel
4.3BlanchardandKahn'sMethod
4.4Sims'Method
4.5Klein'sMethod
4.6AnUndeterminedCoefficientsApproach
5NonlinearSolutionTechniques
5.1ProjectionMethods
5.1.1Overview
5.1.2FiniteElementMethods
5.1.3OrthogonalPolynomials
5.1.4Implementation
5.1.5Extensiontothe/-dimensionalCase
5.1.6ApplicationtotheOptimalGrowthModel
5.2IterationTechniques:Value-FunctionandPolicy-FunctionIterations
5.2.1DynamicProgramming
5.2.2Value-FunctionIterations
5.2.3Policy-FunctionIterations
5.3PerturbationTechniques
5.3.1Notation
5.3.2Overview
5.3.3ApplicationtoDSGEModels
5.3.4ApplicationtoanAsset-PricingModel

PartIIIDataPreparationandRepresentation
6RemovingTrendsandIsolatingCycles
6.1RemovingTrends
6.2IsolatingCycles
6.2.1MathematicalBackground
6.2.2CramtrRepresentations
6.2.3Spectra
6.2.4UsingFilterstoIsolateCycles
6.2.5TheHodrick-PrescottFilter
6.2.6SeasonalAdjustment
6.2.7BandPassFilters
6.3Spuriousness
7SummarizingTimeSeriesBehaviorWhenAllVariablesAreObservable
7.1TwoUsefulReduced-FormModels
7.1.1TheARMAModel
7.1.2AllowingforHeteroskedasticInnovations
7.1.3TheVARModel
7.2SummaryStatistics
7.2.1DeterminingLagLengths
7.2.2CharacterizingthePrecisionofMeasurements
7.3ObtainingTheoreticalPredictionsofSummaryStatistics
8State-SpaceRepresentations
8.1Introduction
8.1.1ARMAModels
8.2DSGEModelsasState-SpaceRepresentations
8.3OverviewofLikelihoodEvaluationandFiltering
8.4TheKalmanFilter
8.4.1Background
8.4.2TheSequentialAlgorithm
8.4.3Smoothing
8.4.4SeriallyCorrelatedMeasurementErrors
8.5ExamplesofReduced-FormState-SpaceRepresentations
8.5.1Time-VaryingParameters
8.5.2StochasticVolatility
8.5.3RegimeSwitching
8.5.4DynamicFactorModels

PartIVMonteCarloMethods
9MonteCarloIntegration:TheBasics
9.1MotivationandOverview
9.2DirectMonteCarloIntegration
9.2.1ModelSimulation
9.2.2PosteriorInferenceviaDirectMonteCarloIntegration
9.3ImportanceSampling
9.3.1AchievingEfficiency:AFirstPass
9.4EfficientImportanceSampling
9.5MarkovChainMonteCarloIntegration
9.5.1TheGibbsSampler
9.5.2Metropolis-HastingsAlgorithms
10LikelihoodEvaluationandFilteringinState-SpaceRepresentationsUsingSequentialMonteCarloMethods
10.1Background
10.2UnadaptedFilters
10.3ConditionallyOptimalFilters
10.4UnconditionalOptimality:TheEISFilter
10.4.1DegenerateTransitions
10.4.2InitializingtheImportanceSampler
10.4.3Example
……
PartVEmpiricalMethods