Aggregation and the Microfoundations of Dynamic Macroeconomics

Hardcover | October 1, 1997

byMario Forni, Marco Lippi

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This book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. Standard models start with intertemporally maximizing agents and obtain dynamic equations linking economic variables like consumption, income, investment interest rate and employment. Suchequations exhibit testable properties like cointegration, definite patterns of Granger causality, and restrictions on the parameters. The usual simplification that agents are identical leads to testing these properties directly on aggregate data. Here this simplification is systematically questioned. In Part I the homogeneity assumption is tested using disaggregate data and strongly rejected. As shown in Part II, the consequence of introducing heterogeneity is that, apart from flukes, cointegration unidirectional Granger causality,restrictions on parameters do not survive aggregation: thus the claim that modern macroeconomics has solid microfoundations is unwarranted. However, it is argued in Part III that aggregation is not necessarily bad. 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.

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This book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. Standard models start with intertemporally maximizing agents and obtain dynamic equations linking economic variables like consumption, income, investment interest rate and employment. Suchequations exhibit testable properties like co...

Mario Forni is at University of Modena. Marco Lippi is at Universita di Roma "La Sapienza".
Format:HardcoverDimensions:254 pages, 9.21 × 6.14 × 0.79 inPublished:October 1, 1997Publisher:Oxford University Press

The following ISBNs are associated with this title:

ISBN - 10:019828800X

ISBN - 13:9780198288008

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Table of Contents

IntroductionList of SymbolsI. AGGREGATION OF SCALAR PROCESSES1. Common and Idiosyncratic Components1.1. The Model for the Individual Variables1.2. A Large Number of Agents1.3. Large Numbers: a General Result1.4. A Continuum of Agents1.5. Autoregressive Relationships among the Microvariables1.6. Bibliographic Notes2. How Many Common Shocks?2.1. Perfect Correlation2.2. Pairwise Singularity2.3. Pairwise Cointegration2.4. How Many Common Shocks?2.5. Dynamic Principal Components2.6. Further Empirical Evidence2.7. Bibliographic Notes3. The Regional Model3.1. From the Individual to the Regional Model3.2. Specification of the Regional Model3.3. Estimation and Diagnostic Checking3.4. Identification of the Common Shocks3.5. Bibliographic Notes4. Aggregating the Common Components4.1. The Wold Representation of the Macrovariable4.2. Identification of the Microparameters4.3. Bibliographic NotesII. AGGREGATION OF ECONOMIC MODELS5. Reformulation of Standard Representative-Agent Models5.1. Life Cycle, Permanent Income under Rational Expectations5.2. A Labor Demand Schedule under Rational Expectations5.3. Consumption and Income Again: Error Correction Mechanisms5.4. Rules of Thumb. Non-Fully Rational, Routinized Behaviors5.5. Structural VAR Models. General Equilibrium5.6. Bibliographic Notes6. The Disaggregated Model6.1. The Microparameter Space6.2. The Micromodel6.3. The Population Space6.4. The Disaggregated Model6.5. Further Comments on the Micromodel. Analytic Functions6.6. Negligible Subsets. The Alternative Principle6.7. Non-Redundancy of the Common Shocks6.8. Dependent and Independent Variables6.9. The Micromodel Coefficients as Analytic Functions6.10. Bibliographic Notes7. The Aggregate Model7.1. Definition of the Aggregate Model7.2. Dropping the Idiosyncratic Component7.3. Aggregation of the DI Model7.4. Macrovariables in the Micromodel. General Equilibrium7.5. Populations and Distributions over [TYPE IN SYMBOL]7.6. Restrictions and Subsets of the Population Space7.7. Bibliographic Notes8. The Rank of the Aggregate Vector8.1. General Statements8.2. The Two-Point Example8.3. A Theorem for the DI Model8.4. More on the Subset of [TYPE IN SYMBOL] where the Model is Singular8.5. Bibliographic Notes9. Cointegration9.1. General Results9.2. Log-Linear Models9.3. An Observation on the Alternative Principle9.4. Bibliographic Notes10. An Extension of the Alternative Principle10.1. From the Spectral Density to the Wold Representation10.2. An Extension of the Alternative Principle10.3. Bibliographic Notes11. Granger Causality11.1. General Results11.2. Discussion of the Two-Point Example11.3. Bibliographic Notes12. Wold Representation: VAR and ARMAX Models12.1. Var Models12.2. Fundamentalness12.3. ARMAX Models12.4. Interpretation. Overidentifying Restrictions12.5. Bibliographic NotesIII. MACROECONOMIC APPLICATIONS13. Permanent Income and the Error Correction Mechanism13.1. Excess Sensitivity13.2. Cointegration of Consumption and Total Income13.3. Singularity13.4. Consumption Volatility13.5. Complete Information and the Representative Agent13.6. An Explanation for Sensitivity and Smoothness13.7. Micro and Macro Singularity13.8. Reconciling PIH and ECM13.9. An Empirical Exercise13.10. Bibliographic Notes14. Disaggregating the Business Cycle14.1. The Number of Common Shocks14.2. Identification of the Common Technology Shock14.3. Estimation of the Sectoral Model14.4. Diagnostic Checking, Data Sources, and Data Treatment14.5. SummaryConclusionsAppendix. Elements of Discrete Time Series TheoryA.1. Orthogonal ProjectionsA.2. The Wold RepresentationA.3. MA Representations of Regular ProcessesA.4. Non-Fundamentalness and PredictionA.5. Scalar ARMA ProcessesA.6. Vector ProcessesA.7. The Spectral DensityA.8. Granger Causality and Sims's TheoremA.9. Bibliographic NotesReferencesIndex