In this PhD thesis I investigate the implications of heterogeneity and aggregation in macroeconomic models. The importance of aggregation lies ~n the fact that when heterogeneity is allowed, we cannot expect macro models to have the same characteristics as the underlying micro models. In particular, a direct consequence of aggregation is that the dynamic properties of the micro model do not hold in general for the macr,? model. Despite this problem, modern macroeconomics tends to model aggregate data alone, through the construction of models where the individual consumer or firm is related to aggregate data under the guise of a 'representative agent'. In this thesis, I present a heterogeneous real business cycle model where I allow for cross sectional heterogeneity in the dynamics of the firm productivities. I show that heterogeneity allows the model to generate very persistent dynamics that can mimic impressively those of actual data. This is because, the dynamics of the model are now the result of the interactions between heterogeneous firms. Another problem that often arises with heterogeneity is that through aggregation, the dynamics that describe the co-movements between two variables can be more persistent and complex than the dynamics observed for the individual behaviour. Standard co-integration techniques are not able to deal with such persistent co-movements since they cannot distinguish between persistent deviations from the equilibrium and spurious relations. Therefore, many intuitive economic relations are often empirically rejected. To this purpose, I introduce in the thesis a methodology which can test robustly for co-integration between two variables, which deviate persistently from their long-run equilibrium. I test for a co-integration in the Uncovered Interest Parity and the Purchasing Power Parity with my approach and, unlike the standard approaches, it does not reject the hypothesis that they hold in the long run.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:485841 |
Date | January 2006 |
Creators | Moretti, Gianluca |
Publisher | University of York |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://etheses.whiterose.ac.uk/11064/ |
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