Methods for applied macroeconomic research

The last twenty years have witnessed tremendous advances in the mathematical, statistical, and computational tools available to applied macroeconomists. This rapidly evolving field has redefined how researchers test models and validate theories. Yet until now there has been no textbook that unites the latest methods and bridges the divide between theoretical and applied work.

Fabio Canova brings together dynamic equilibrium theory, data analysis, and advanced econometric and computational methods to provide the first comprehensive set of techniques for use by academic economists as well as professional macroeconomists in banking and finance, industry, and government. This graduate-level textbook is for readers knowledgeable in modern macroeconomic theory, econometrics, and computational programming using RATS, MATLAB, or Gauss. Inevitably a modern treatment of such a complex topic requires a quantitative perspective, a solid dynamic theory background, and the development of empirical and numerical methods--which is where Canova's book differs from typical graduate textbooks in macroeconomics and econometrics. Rather than list a series of estimators and their properties, Canova starts from a class of DSGE models, finds an approximate linear representation for the decision rules, and describes methods needed to estimate their parameters, examining their fit to the data. The book is complete with numerous examples and exercises.

Today's economic analysts need a strong foundation in both theory and application. Methods for Applied Macroeconomic Research offers the essential tools for the next generation of macroeconomists.

Author Notes

Fabio Canova is ICREA Research Professor at the University of Pompeu Fabra in Barcelona and Fellow of the Centre for Economic Policy Research in London.

Preface	p. xi
1 Preliminaries	p. 1
1.1 Stochastic Processes	p. 2
1.2 Convergence Concepts	p. 3
1.3 Time Series Concepts	p. 8
1.4 Laws of Large Numbers	p. 14
1.5 Central Limit Theorems	p. 16
1.6 Elements of Spectral Analysis	p. 18
2 DSGE Models, Solutions, and Approximations	p. 26
2.1 A Few Useful Models	p. 27
2.2 Approximation Methods	p. 45
3 Extracting and Measuring Cyclical Information	p. 70
3.1 Statistical Decompositions	p. 72
3.2 Hybrid Decompositions	p. 83
3.3 Economic Decompositions	p. 100
3.4 Time Aggregation and Cycles	p. 104
3.5 Collecting Cyclical Information	p. 105
4 VAR Models	p. 111
4.1 The Wold Theorem	p. 112
4.2 Specification	p. 118
4.3 Moments and Parameter Estimation of a VAR(q)	p. 126
4.4 Reporting VAR Results	p. 130
4.5 Identification	p. 141
4.6 Problems	p. 151
4.7 Validating DSGE Models with VARs	p. 159
5 GMM and Simulation Estimators	p. 165
5.1 Generalized Method of Moments and Other Standard Estimators	p. 166
5.2 IV Estimation in a Linear Model	p. 169
5.3 GMM Estimation; An Overview	p. 176
5.4 GMM Estimation of DSGE Models	p. 191
5.3 Simulation Estimators	p. 197
6 Likelihood Methods	p. 212
6.1 The Kalman Filter	p. 214
6.2 The Prediction Error Decomposition of Likelihood	p. 221
6.3 Numerical Tips	p. 228
6.4 ML Estimation of DSGE Models	p. 230
6.5 Two Examples	p. 240
7 Calibration	p. 248
7.1 A Definition	p. 249
7.2 The Uncontroversial Parts	p. 250
7.3 Choosing Parameters and Stochastic Processes	p. 252
7.4 Model Evaluation	p. 259
7.5 The Sensitivity of the Measurement	p. 279
7.6 Savings, Investments, and Tax Cuts: An Example	p. 282
8 Dynamic Macro Panels	p. 288
8.1 From Economic Theory to Dynamic Panels	p. 289
8.2 Panels with Homogeneous Dynamics	p. 291
8.3 Dynamic Heterogeneity	p. 304
8.4 To Pool or Not to Pool?	p. 315
8.5 Is Money Superneutral?	p. 321
9 Introduction to Bayesian Methods	p. 325
9.1 Preliminaries	p. 326
9.2 Decision Theory	p. 335
9.3 Inference	p. 336
9.4 Hierarchical and Empirical Bayes Models	p. 345
9.5 Posterior Simulators	p. 353
9.6 Robustness	p. 370
9.7 Estimating Returns to Scale in Spain	p. 370
10 Bayesian VARs	p. 373
10.1 The Likelihood Function of an m-Variable VAR(q)	p. 374
10.2 Priors for VARs	p. 376
10.3 Structural BVARs	p. 390
10.4 Time-Varying-Coefficient BVARs	p. 397
10.5 Panel VAR Models	p. 404
11 Bayesian Time Series and DSGE Models	p. 418
11.1 Factor Models	p. 449
11.2 Stochastic Volatility Models	p. 427
11.3 Markov Switching Models	p. 433
11.4 Bayesian DSGE Models	p. 440
Appendix A Statistical Distributions	p. 463
References	p. 469
Index	p. 487

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