《结构宏观计量经济学（第2版）》编著者德容。

本书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。这是第二版，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。目次：（一）导论：背景综述；典范型中的铸造模型；DSGE模型的三个模型；（二）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；（三）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；（五）经验方法：校准；矩匹配；极大似然；贝叶斯方法。

Preface

Preface to the First Edition

Part I Introduction

1 Background and Overview

 1.1 Background

 1.2 Overview

2 Casting Models in Canonical Form

 2.1 Notation

 2.1.1 Log-Linear Model Representations

 2.1.2 Nonlinear Model Representations

 2.2 Linearization

 2.2.1 Taylor Series Approximation

 2.2.2 Log-Linear Approximations

 2.2.3 Example Equations

3 DSGE Models: Three Examples

 3.1 Model I: A Real Business Cycle Model

 3.1.1 Environment

 3.1.2 The Nonlinear System

 3.1.3 Log-Linearization

 3.2 Model II: Monopolistic Competition and Monetary Policy

 3.2.1 Environment

 3.2.2 The Nonlinear System

 3.2.3 Log-Linearization

 3.3 Model III: Asset Pricing

 3.3.1 Single-Asset Environment

 3.3.2 Multi-Asset Environment

 3.3.3 Alternative Preference Specifications

Part II Model Solution Techniques

4 Linear Solution Techniques

 4.1 Homogeneous Systems

 4.2 Example Models

 4.2.1 The Optimal Consumption Model

 4.2.2 Asset Pricing with Linear Utility

 4.2.3 Ramsey's Optimal Growth Model

 4.3 Blanchard and Kahn's Method

 4.4 Sims' Method

 4.5 Klein's Method

 4.6 An Undetermined Coefficients Approach

5 Nonlinear Solution Techniques

 5.1 Projection Methods

 5.1.1 Overview

 5.1.2 Finite Element Methods

 5.1.3 Orthogonal Polynomials

 5.1.4 Implementation

 5.1.5 Extension to the/-dimensional Case

 5.1.6 Application to the Optimal Growth Model

 5.2 Iteration Techniques: Value-Function and Policy-Function Iterations

 5.2.1 Dynamic Programming

 5.2.2 Value-Function Iterations

 5.2.3 Policy-Function Iterations

 5.3 Perturbation Techniques

 5.3.1 Notation

 5.3.2 Overview

 5.3.3 Application to DSGE Models

 5.3.4 Application to an Asset-Pricing Model

Part III Data Preparation and Representation

6 Removing Trends and Isolating Cycles

 6.1 Removing Trends

 6.2 Isolating Cycles

 6.2.1 Mathematical Background

 6.2.2 Cramtr Representations

 6.2.3 Spectra

 6.2.4 Using Filters to Isolate Cycles

 6.2.5 The Hodrick-Prescott Filter

 6.2.6 Seasonal Adjustment

 6.2.7 Band Pass Filters

 6.3 Spuriousness

7 Summarizing Time Series Behavior When All Variables

 Are Observable

 7.1 Two Useful Reduced-Form Models

 7.1.1 The ARMA Model

 7.1.2 Allowing for Heteroskedastic Innovations

 7.1.3 The VAR Model

 7.2 Summary Statistics

 7.2.1 Determining Lag Lengths

 7.2.2 Characterizing the Precision of Measurements

 7.3 Obtaining Theoretical Predictions of Summary Statistics

8 State-Space Representations

 8.1 Introduction

 8.1.1 ARMA Models

 8.2 DSGE Models as State-Space Representations

 8.3 Overview of Likelihood Evaluation and Filtering

 8.4 The Kalman Filter

 8.4.1 Background

 8.4.2 The Sequential Algorithm

 8.4.3 Smoothing

 8.4.4 Serially Correlated Measurement Errors

 8.5 Examples of Reduced-Form State-Space Representations

 8.5.1 Time-Varying Parameters

 8.5.2 Stochastic Volatility

 8.5.3 Regime Switching

 8.5.4 Dynamic Factor Models

Part IV Monte Carlo Methods

9 Monte Carlo Integration: The Basics

 9.1 Motivation and Overview

 9.2 Direct Monte Carlo Integration

 9.2.1 Model Simulation

 9.2.2 Posterior Inference via Direct Monte Carlo Integration

 9.3 Importance Sampling

 9.3.1 Achieving Efficiency: A First Pass

 9.4 Efficient Importance Sampling

 9.5 Markov Chain Monte Carlo Integration

 9.5.1 The Gibbs Sampler

 9.5.2 Metropolis-Hastings Algorithms

10 Likelihood Evaluation and Filtering in State-Space

 Representations Using Sequential Monte Carlo Methods

 10.1 Background

 10.2 Unadapted Filters

 10.3 Conditionally Optimal Filters

 10.4 Unconditional Optimality: The EIS Filter

 10.4.1 Degenerate Transitions

 10.4.2 Initializing the Importance Sampler

 10.4.3 Example