Aggregation and the Microfoundations of Dynamic MacroeconomicsThrough careful methodological analysis, this book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. In Part I, the authors test and reject the homogeneity assumption using disaggregate data. In Part II, they demonstrate that apart from random flukes, cointegration unidirectional Granger causality and restrictions on parameters do not survive aggregation when heterogeneity is introduced. They conclude that the claim that modern macroeconomics has solid microfoundations is unwarranted. However, some important theory-based models that do not fit aggregate data well in their representative-agent version can be reconciled with aggregate data by introducing heterogeneity. |
Contents
1 | 4 |
How Many Common Shocks? | 19 |
The Regional Model | 43 |
Aggregating the Common Components | 53 |
Reformulation of Standard RepresentativeAgent Models | 67 |
The Disaggregated Model | 79 |
The Aggregate Model | 105 |
The Rank of the Aggregate Vector | 116 |
Common terms and phrases
agents aggregate model aggregate vector Alternative Principle analysis analytic functions ARMA ARMAX assume assumption autocorrelation business cycle Chapter cointegration cointegration vector common component common shocks comovements consider consumers consumption change converges correlation covariance defined definition density matrix dynamic Econometrics Economic empirical equal equation estimated example finite firstly fundamental fundamental representation Granger causality h₁ idiosyncratic component implies income and consumption income changes individual labor income lag operator leading coefficients Lebesgue measure Lippi macro macroeconomic macroequation matrix micro cointegration micromodel microparameters microvariables modulus smaller Moreover negligible subset non-redundant non-singular number of common obtained orthogonal output parameters polynomials rank Rational Expectations rational functions representative-agent roots scalar sequence singular spectral density square summable stationary stochastic technology shock Theorem tion total income unity vanish variance vector processes white noise Wold representation zero