结构宏观计量经济学
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浙江杭州
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作者[美] 德容（David N.DeJong） 著
出版社世界图书出版公司
ISBN9787510058226
出版时间2013-03
版次1
装帧平装
开本16开
纸张胶版纸
页数418页
定价69元
上书时间2024-08-01
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基本信息
书名:结构宏观计量经济学
定价：69元
作者:[美] 德容（David N.DeJong） 著
出版社：世界图书出版公司
出版日期：2013-03-01
ISBN：9787510058226
字数：
页码：418
版次：2
装帧：平装
开本：24开
商品重量：
编辑推荐
《结构宏观计量经济学(第2版)》由世界图书出版公司北京公司出版。
内容提要
《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
PrefacePreface to the First EditionPart I Introduction1 Background and Overview1.1 Background1.2 Overview2 Casting Models iCanonical Form2.1 Notation2.1.1 Log-Linear Model Representations2.1.2 Nonlinear Model Representations2.2 Linearization2.2.1 Taylor Series Appromation2.2.2 Log-Linear Appromations2.2.3 Example Equations3 DSGE Models: Three Examples3.1 Model I: A Real Business Cycle Model3.1.1 Environment3.1.2 The Nonlinear System3.1.3 Log-Linearization3.2 Model II: Monopolistic Competitioand Monetary Policy3.2.1 Environment3.2.2 The Nonlinear System3.2.3 Log-Linearization3.3 Model III: Asset Pricing3.3.1 Single-Asset Environment3.3.2 Multi-Asset Environment3.3.3 Alternative Preference SpecificationsPart II Model SolutioTechniques4 Linear SolutioTechniques4.1 Homogeneous Systems4.2 Example Models4.2.1 The Optimal ConsumptioModel4.2.2 Asset Pricing with Linear Utility4.2.3 Ramsey's Optimal Growth Model4.3 Blanchard and Kahn's Method4.4 Sims' Method4.5 Klein's Method4.6 AUndetermined Coefficients Approach5 Nonlinear SolutioTechniques5.1 ProjectioMethods5.1.1 Overview5.1.2 Finite Element Methods5.1.3 Orthogonal Polynomials5.1.4 Implementation5.1.5 Extensioto the/-dimensional Case5.1.6 Applicatioto the Optimal Growth Model5.2 IteratioTechniques: Value-Functioand Policy-FunctioIterations5.2.1 Dynamic Programming5.2.2 Value-FunctioIterations5.2.3 Policy-FunctioIterations5.3 PerturbatioTechniques5.3.1 Notation5.3.2 Overview5.3.3 Applicatioto DSGE Models5.3.4 Applicatioto aAsset-Pricing ModelPart III Data Preparatioand Representation6 Removing Trends and Isolating Cycles6.1 Removing Trends6.2 Isolating Cycles6.2.1 Mathematical Background6.2.2 Cramtr Representations6.2.3 Spectra6.2.4 Using Filters to Isolate Cycles6.2.5 The Hodrick-Prescott Filter6.2.6 Seasonal Adjustment6.2.7 Band Pass Filters6.3 Spuriousness7 Summarizing Time Series Behavior WheAll Variables Are Observable7.1 Two Useful Reduced-Form Models7.1.1 The ARMA Model7.1.2 Allowing for Heteroskedastic Innovations7.1.3 The VAR Model7.2 Summary Statistics7.2.1 Determining Lag Lengths7.2.2 Characterizing the Precisioof Measurements7.3 Obtaining Theoretical Predictions of Summary Statistics8 State-Space Representations8.1 Introduction8.1.1 ARMA Models8.2 DSGE Models as State-Space Representations8.3 Overview of Likelihood Evaluatioand Filtering8.4 The KalmaFilter8.4.1 Background8.4.2 The Sequential Algorithm8.4.3 Smoothing8.4.4 Serially Correlated Measurement Errors8.5 Examples of Reduced-Form State-Space Representations8.5.1 Time-Varying Parameters8.5.2 Stochastic Volatility8.5.3 Regime Switching8.5.4 Dynamic Factor ModelsPart IV Monte Carlo Methods9 Monte Carlo Integration: The Basics9.1 Motivatioand Overview9.2 Direct Monte Carlo Integration9.2.1 Model Simulation9.2.2 Posterior Inference via Direct Monte Carlo Integration9.3 Importance Sampling9.3.1 Achieving Efficiency: A First Pass9.4 Efficient Importance Sampling9.5 Markov ChaiMonte Carlo Integration9.5.1 The Gibbs Sampler9.5.2 Metropolis-Hastings Algorithms10 Likelihood Evaluatioand Filtering iState-Space Representations Using Sequential Monte Carlo Methods10.1 Background10.2 Unadapted Filters10.3 Conditionally Optimal Filters10.4 Unconditional Optimality: The EIS Filter10.4.1 Degenerate Transitions10.4.2 Initializing the Importance Sampler10.4.3 Example……Part V Empirical Methods
作者介绍
作者：（美国）德容（David N.Dejong）
序言
PrefacePreface to the First EditionPart I Introduction1 Background and Overview1.1 Background1.2 Overview2 Casting Models iCanonical Form2.1 Notation2.1.1 Log-Linear Model Representations2.1.2 Nonlinear Model Representations2.2 Linearization2.2.1 Taylor Series Approximation2.2.2 Log-Linear Approximations2.2.3 Example Equations3 DSGE Models: Three Examples3.1 Model I: A Real Business Cycle Model3.1.1 Environment3.1.2 The Nonlinear System3.1.3 Log-Linearization3.2 Model II: Monopolistic Competitioand Monetary Policy3.2.1 Environment3.2.2 The Nonlinear System3.2.3 Log-Linearization3.3 Model III: Asset Pricing3.3.1 Single-Asset Environment3.3.2 Multi-Asset Environment3.3.3 Alternative Preference SpecificationsPart II Model SolutioTechniques4 Linear SolutioTechniques4.1 Homogeneous Systems4.2 Example Models4.2.1 The Optimal ConsumptioModel4.2.2 Asset Pricing with Linear Utility4.2.3 Ramsey's Optimal Growth Model4.3 Blanchard and Kahn's Method4.4 Sims' Method4.5 Klein's Method4.6 AUndetermined Coefficients Approach5 Nonlinear SolutioTechniques5.1 ProjectioMethods5.1.1 Overview5.1.2 Finite Element Methods5.1.3 Orthogonal Polynomials5.1.4 Implementation5.1.5 Extensioto the/-dimensional Case5.1.6 Applicatioto the Optimal Growth Model5.2 IteratioTechniques: Value-Functioand Policy-FunctioIterations5.2.1 Dynamic Programming5.2.2 Value-FunctioIterations5.2.3 Policy-FunctioIterations5.3 PerturbatioTechniques5.3.1 Notation5.3.2 Overview5.3.3 Applicatioto DSGE Models5.3.4 Applicatioto aAsset-Pricing ModelPart III Data Preparatioand Representation6 Removing Trends and Isolating Cycles6.1 Removing Trends6.2 Isolating Cycles6.2.1 Mathematical Background6.2.2 Cramtr Representations6.2.3 Spectra6.2.4 Using Filters to Isolate Cycles6.2.5 The Hodrick-Prescott Filter6.2.6 Seasonal Adjustment6.2.7 Band Pass Filters6.3 Spuriousness7 Summarizing Time Series Behavior WheAll Variables Are Observable7.1 Two Useful Reduced-Form Models7.1.1 The ARMA Model7.1.2 Allowing for Heteroskedastic Innovations7.1.3 The VAR Model7.2 Summary Statistics7.2.1 Determining Lag Lengths7.2.2 Characterizing the Precisioof Measurements7.3 Obtaining Theoretical Predictions of Summary Statistics8 State-Space Representations8.1 Introduction8.1.1 ARMA Models8.2 DSGE Models as State-Space Representations8.3 Overview of Likelihood Evaluatioand Filtering8.4 The KalmaFilter8.4.1 Background8.4.2 The Sequential Algorithm8.4.3 Smoothing8.4.4 Serially Correlated Measurement Errors8.5 Examples of Reduced-Form State-Space Representations8.5.1 Time-Varying Parameters8.5.2 Stochastic Volatility8.5.3 Regime Switching8.5.4 Dynamic Factor ModelsPart IV Monte Carlo Methods9 Monte Carlo Integration: The Basics9.1 Motivatioand Overview9.2 Direct Monte Carlo Integration9.2.1 Model Simulation9.2.2 Posterior Inference via Direct Monte Carlo Integration9.3 Importance Sampling9.3.1 Achieving Efficiency: A First Pass9.4 Efficient Importance Sampling9.5 Markov ChaiMonte Carlo Integration9.5.1 The Gibbs Sampler9.5.2 Metropolis-Hastings Algorithms10 Likelihood Evaluatioand Filtering iState-Space Representations Using Sequential Monte Carlo Methods10.1 Background10.2 Unadapted Filters10.3 Conditionally Optimal Filters10.4 Unconditional Optimality: The EIS Filter10.4.1 Degenerate Transitions10.4.2 Initializing the Importance Sampler10.4.3 Example……Part V Empirical Methods
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