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<br></P><P>METHODS FOR APPLIED MACROECONOMIC RESEARCH ,2007</P><P><TABLE cellSpacing=1 cellPadding=1 width="100%" align=left><TR><TD align=right>出版社:</TD><TD><a href="http://st-ebook.com.tw/publisherlist.aspx?pbsno=0118" target="_blank" >PRINCETON UNIVERSITY PRESS </A></TD></TR><TR><TD align=right>作者:</TD><TD>Fabio CANOVA (Universitat Pompeu Fabra, PhD University of Minnesota)</TD></TR><TR><TD align=right><P>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.<br><br>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.<br><br>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.<br><br>TABLE OF CONTENTS:<br><br>Preface xi<br><br>Chapter 1: Preliminaries 1<br>1.1 Stochastic Processes 2<br>1.2 Convergence Concepts 3<br>1.3 Time Series Concepts 8<br>1.4 Laws of Large Numbers 14<br>1.5 Central Limit Theorems 16<br>1.6 Elements of Spectral Analysis 18<br><br><br>Chapter 2: DSGE Models, Solutions, and Approximations 26<br>2.1 A Few Useful Models 27<br>2.2 Approximation Methods 45<br><br><br>Chapter 3: Extracting and Measuring Cyclical Information 70<br>3.1 Statistical Decompositions 72<br>3.2 Hybrid Decompositions 83<br>3.3 Economic Decompositions 100<br>3.4 Time Aggregation and Cycles 104<br>3.5 Collecting Cyclical Information 105<br><br><br>Chapter 4: VAR Models 111<br>4.1 TheWold Theorem 112<br>4.2 Specification 118<br>4.3 Moments and Parameter Estimation of a VAR.q/ 126<br>4.4 Reporting VAR Results 130<br>4.5 Identification 141<br>4.6 Problems 151<br>4.7 Validating DSGE Models with VARs 159<br><br><br>Chapter 5: GMM and Simulation Estimators 165<br>5.1 Generalized Method of Moments and Other Standard Estimators 166<br>5.2 IV Estimation in a Linear Model 169<br>5.3 GMM Estimation: An Overview 176<br>5.4 GMM Estimation of DSGE Models 191<br>5.5 Simulation Estimators 197<br><br><br>Chapter 6: Likelihood Methods 212<br>6.1 The Kalman Filter 214<br>6.2 The Prediction Error Decomposition of Likelihood 221<br>6.3 Numerical Tips 228<br>6.4 ML Estimation of DSGE Models 230<br>6.5 Two Examples 240<br><br><br>Chapter 7: Calibration 248<br>7.1 A Definition 249<br>7.2 The Uncontroversial Parts 250<br>7.3 Choosing Parameters and Stochastic Processes 252<br>7.4 Model Evaluation 259<br>7.5 The Sensitivity of the Measurement 279<br>7.6 Savings, Investments, and Tax Cuts: An Example 282<br><br><br>Chapter 8: Dynamic Macro Panels 288<br>8.1 From Economic Theory to Dynamic Panels 289<br>8.2 Panels with Homogeneous Dynamics 291<br>8.3 Dynamic Heterogeneity 304<br>8.4 To Pool or Not to Pool? 315<br>8.5 Is Money Superneutral? 321<br><br><br>Chapter 9: Introduction to Bayesian Methods 325<br>9.1 Preliminaries 326<br>9.2 Decision Theory 335<br>9.3 Inference 336<br>9.4 Hierarchical and Empirical Bayes Models 345<br>9.5 Posterior Simulators 353<br>9.6 Robustness 370<br>9.7 Estimating Returns to Scale in Spain 370<br><br><br>Chapter 10: Bayesian VARs 373<br>10.1 The Likelihood Function of an m-Variable VAR(q) 374<br>10.2 Priors for VARs 376<br>10.3 Structural BVARs 390<br>10.4 Time-Varying-Coefficient BVARs 397<br>10.5 Panel VAR Models 404<br><br><br>Chapter 11: Bayesian Time Series and DSGE Models 418<br>11.1 Factor Models 419<br>11.2 Stochastic Volatility Models 427<br>11.3 Markov Switching Models 433<br>11.4 Bayesian DSGE Models 440<br><br><br>Appendix A Statistical Distributions 463<br><br><br>References 469<br>Index 487</P></TD><TD><P> </P></TD></TR></TABLE></P>
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