E-Book Overview
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.
E-Book Content
Contents Chapter 1: Preliminaries 1.1 Stochastic Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Concepts of Convergence . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Almost sure (a.s.) convergence . . . . . . . . . . . . . . . . . 1.2.2 Convergence in Probability . . . . . . . . . . . . . . . . . . . . 1.2.3 Convergence in Lq -norm. . . . . . . . . . . . . . . . . . . . . . 1.2.4 Convergence in Distribution . . . . . . . . . . . . . . . . . . . 1.3 Time Series Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Law of Large Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Dependent and Identically Distributed Observations . . . . 1.4.2 Dependent and Heterogeneously Distributed Observations 1.4.3 Martingale Difference Process . . . . . . . . . . . . . . . . . . 1.5 Central Limit Theorems . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Dependent and Identically Distributed Observations . . . . 1.5.2 Dependent Heterogeneously Distributed Observations . . . 1.5.3 Martingale Difference Observations . . . . . . . . . . . . . . . 1.6 Elements of Spectral Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 3 3 4 6 7 8 14 14 15 16 17 17 18 18 19 Chapter 2: DSGE Models, Solutions and Approximations 2.1 Few useful models . . . . . . . . . . . . . . . . . . . . . 2.1.1 A basic Real Business Cycle (RBC) Model . 2.1.2 Heterogeneous agent models . . . . . . . . . . 2.1.3 Monetary Models . . . . . . . . . . . . . . . . . 2.2 Approximation methods . . . . . . . . . . . . . . . . . 2.2.1 Quadratic approximations . . . . . . . . . . . . 2.2.2 Discretization . . . . . . . . . . . . . . . . . . . . 2.2.3 Log linear Approximations . . . . . . . . . . . 2.2.4 Second order approximations . . . . . . . . . . . . 2.2.5 Parametrizing expectations . . . . . . . . . . . 2.2.6 A Comparison of methods . . . . . . . . . . . . . . . . . . . . . . 27 28 28 35 38 44 45 48 51 60 62 65 i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .