Macroeconometrics and Time Series Analysis

by Durlauf, Steven N.; Blume, Lawrence E.

• ISBN13: 9780230238848
• ISBN10: 023023884X
• Format: Hardcover
• Copyright: 2009-12-15
• Publisher: Palgrave Macmillan

Summary

Time series methodology represents the language for much of empirical economics. The entries in this volume cover both the mathematical foundations of time series as well as their application in a range of contexts, in particular macroeconomics. The entries reflect both the increasing mathematical sophistication of time series econometrics as well as advances in understanding how these tools can elucidate fundamental economic questions. Book jacket.

Author Biography

STEVEN N. DURLAUF is the Kenneth J. Arrow Professor of Economics at the University of Wisconsin at Madison, USA. He has served as Co-Director of the Economics Program of the Santa Fe Institute and is currently a Research Associate of the National Bureau of Economic Research. A Fellow of the Econometric Society, Durlauf's research covers a range of topics in macroeconomics, econometrics, and income inequality. He received a BA in Economics from Harvard in 1980 and his PhD in Economics from Yale in 1986.

LAWRENCE E BLUME is Goldwin Smith Professor of Economics at Cornell University, USA. He is a member of the external faculty at the Santa Fe Institute, where he has served as Co-Director of the Economics Program and on the Institute's steering committee. He teaches and conducts research in general equilibrium theory and game theory, and also has research projects on natural resource management and network design. A Fellow of the Econometric Society, he received an AB in Economics from Washington University and a PhD in Economics from Berkeley.

Table of Contents

List of Contributors	p. vii
General Preface	p. ix
Introduction	p. x
Aggregation (econometrics)	p. 1
ARCH models	p. 15
Bayesian methods in macroeconometrics	p. 28
Bayesian time series analysis	p. 35
Central limit theorems	p. 46
Cointegration	p. 53
Continuous and discrete time models	p. 60
Data filters	p. 68
Equilibrium-correction models	p. 76
Forecasting	p. 90
Fractals	p. 94
Functional central limit theorems	p. 99
Generalized method of moments estimation	p. 105
Granger-Sims causality	p. 119
Heteroskedasticity and autocorrelation corrections	p. 135
Impulse response function	p. 145
Kalman and particle filtering	p. 151
Law(s) of large numbers	p. 158
Long memory models	p. 163
Nonlinear time series analysis	p. 169
Prediction formulas	p. 178
Rational expectations	p. 193
Regime switching models	p. 202
Seasonal adjustment	p. 210
Serial correlation and serial dependence	p. 227
SNP: nonparametric time series analysis	p. 245
Spectral analysis	p. 250
Spline functions	p. 260
Spurious regressions	p. 265
State space models	p. 269
Stochastic volatility models	p. 276
Structrual change, econometrics of	p. 288
Structural vector autoregressions	p. 303
Threshold models	p. 308
Time series analysis	p. 317
Trend/cycle decomposition	p. 343
Unit roots	p. 347
Variance decomposition	p. 369
Varying coefficient models	p. 372
Vector autoregressions	p. 378
Wavelets	p. 391
Index	p. 399
Table of Contents provided by Ingram. All Rights Reserved.

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