结构宏观计量经济学 第2版 英文2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

结构宏观计量经济学 第2版 英文PDF格式电子书版下载

注意：本站所有压缩包均有解压码： 点击下载压缩包解压工具

Part Ⅰ Introduction3

1 Background and Overview3

1.1 Background3

1.2 Overview4

2 Casting Models in Canonical Form9

2.1 Notation9

2.1.1 Log-Linear Model Representations11

2.1.2 Nonlinear Model Representations11

2.2 Linearization12

2.2.1 Taylor Series Approximation12

2.2.2 Log-Linear Approximations14

2.2.3 Example Equations15

3 DSGE Models：Three Examples18

3.1 Model Ⅰ：A Real Business Cycle Model20

3.1.1 Environment20

3.1.2 The Nonlinear System23

3.1.3 Log-Linearization26

3.2 Model Ⅱ：Monopolistic Competition and Monetary Policy28

3.2.1 Environment28

3.2.2 The Nonlinear System33

3.2.3 Log-Linearization34

3.3 Model Ⅲ：Asset Pricing38

3.3.1 Single-Asset Environment38

3.3.2 Multi-Asset Environment39

3.3.3 Alternative Preference Specifications40

Part Ⅱ Model Solution Techniques51

4 Linear Solution Techniques51

4.1 Homogeneous Systems52

4.2 Example Models54

4.2.1 The Optimal Consumption Model54

4.2.2 Asset Pricing with Linear Utility55

4.2.3 Ramsey's Optimal Growth Model56

4.3 Blanchard and Kahn's Method57

4.4 Sims'Method61

4.5 Klein's Method64

4.6 An Undetermined Coefficients Approach66

5 Nonlinear Solution Techniques69

5.1 Projection Methods71

5.1.1 Overview71

5.1.2 Finite Element Methods72

5.1.3 Orthogonal Polynomials73

5.1.4 Implementation74

5.1.5 Extension to the l-dimensional Case78

5.1.6 Application to the Optimal Growth Model79

5.2 Iteration Techniques：Value-Function and Policy-Function Iterations87

5.2.1 Dynamic Programming87

5.2.2 Value-Function Iterations89

5.2.3 Policy-Function Iterations94

5.3 Perturbation Techniques95

5.3.1 Notation95

5.3.2 Overview97

5.3.3 Application to DSGE Models99

5.3.4 Application to an Asset-Pricing Model105

Part Ⅲ Data Preparation and Representation113

6 Removing Trends and Isolating Cycles113

6.1 Removing Trends115

6.2 Isolating Cycles120

6.2.1 Mathematical Background120

6.2.2 Cramér Representations124

6.2.3 Spectra125

6.2.4 Using Filters to Isolate Cycles126

6.2.5 The Hodrick-Prescott Filter128

6.2.6 Seasonal Adjustment130

6.2.7 Band Pass Filters131

6.3 Spuriousness134

7 Summarizing Time Series Behavior When All Variables Are Observable138

7.1 Two Useful Reduced-Form Models139

7.1.1 The ARMA Model139

7.1.2 Allowing for Heteroskedastic Innovations145

7.1.3 The VAR Model147

7.2 Summary Statistics149

7.2.1 Determining Lag Lengths157

7.2.2 Characterizing the Precision of Measurements159

7.3 Obtaining Theoretical Predictions of Summary Statistics162

8 State-Space Representations166

8.1 Introduction166

8.1.1 ARMA Models167

8.2 DSGE Models as State-Space Representations169

8.3 Overview of Likelihood Evaluation and Filtering171

8.4 The Kalman Filter173

8.4.1 Background173

8.4.2 The Sequential Algorithm175

8.4.3 Smoothing178

8.4.4 Serially Correlated Measurement Errors181

8.5 Examples of Reduced-Form State-Space Representations182

8.5.1 Time-Varying Parameters182

8.5.2 Stochastic Volatility185

8.5.3 Regime Switching186

8.5.4 Dynamic Factor Models187

Part Ⅳ Monte Carlo Methods193

9 Monte Carlo Integration：The Basics193

9.1 Motivation and Overview193

9.2 Direct Monte Carlo Integration196

9.2.1 Model Simulation198

9.2.2 Posterior Inference via Direct Monte Carlo Integration201

9.3 Importance Sampling202

9.3.1 Achieving Efficiency：A First Pass206

9.4 Efficient Importance Sampling211

9.5 Markov Chain Monte Carlo Integration215

9.5.1 The Gibbs Sampler216

9.5.2 Metropolis-Hastings Algorithms218

10 Likelihood Evaluation and Filtering in State-Space Representations Using Sequential Monte Carlo Methods221

10.1 Background221

10.2 Unadapted Filters224

10.3 Conditionally Optimal Filters228

10.4 Unconditional Optimality：The EIS Filter233

10.4.1 Degenerate Transitions235

10.4.2 Initializing the Importance Sampler236

10.4.3 Example239

10.5 Application to DSGE Models241

10.5.1 Initializing the Importance Sampler243

10.5.2 Initializing the Filtering Density245

10.5.3 Application to the RBC Model246

Part Ⅴ Empirical Methods253

11 Calibration253

11.1 Historical Origins and Philosophy253

11.2 Implementation258

11.3 The Welfare Cost of Business Cycles261

11.4 Productivity Shocks and Business Cycle Fluctuations268

11.5 The Equity Premium Puzzle273

11.6 Critiques and Extensions276

11.6.1 Critiques276

11.6.2 Extensions279

12 Matching Moments285

12.1 Overview285

12.2 Implementation286

12.2.1 The Generalized Method of Moments286

12.2.2 The Simulated Method of Moments294

12.2.3 Indirect Inference297

12.3 Implementation in DSGE Models300

12.3.1 Analyzing Euler Equations300

12.3.2 Analytical Calculations Based on Linearized Models301

12.3.3 Simulations Involving Linearized Models306

12.3.4 Simulations Involving Nonlinear Approximations307

12.4 Empirical Application：Matching RBC Moments308

13 Maximum Likelihood314

13.1 Overview314

13.2 Introduction and Historical Background316

13.3 A Primer on Optimization Algorithms318

13.3.1 Simplex Methods319

13.3.2 Derivative-Based Methods328

13.4 Ill-Behaved Likelihood Surfaces：Problems and Solutions330

13.4.1 Problems330

13.4.2 Solutions331

13.5 Model Diagnostics and Parameter Stability334

13.6 Empirical Application：Identifying Sources of Business Cycle Fluctuations337

14 Bayesian Methods351

14.1 Overview of Objectives351

14.2 Preliminaries352

14.3 Using Structural Models as Sources of Prior Information for Reduced-Form Analysis355

14.4 Implementing Structural Models Directly360

14.5 Model Comparison361

14.6 Using an RBC Model as a Source of Prior Information for Forecasting364

14.7 Estimating and Comparing Asset-Pricing Models373

14.7.1 Estimates380

14.7.2 Model Comparison384

References387

Index401