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Title: Cross section distribution dynamics
Author: Lamo, Ana Rosa
ISNI:       0000 0001 3604 4636
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 1996
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This thesis contains four chapters. Each chapter constitutes an empirical exercise in which I apply econometric ideas on studying the dynamics of large cross sections of data (Random Fields). Three of them concern the empirics of convergence and the fourth analyses business cycle fluctuations. The first, "Notes on Convergence Empirics: Some Calculations for Spanish Regions," describes the econometric methods for studying the dynamics of the distributions and how to characterise convergence in this framework, explains why the standard cross-section regression analysis is misleading when testing for convergence and then performs some calculations for regions in Spain. The second chapter, "Dynamics of the Income Distribution Across OECD Countries", considers its baseline hypotheses to be those generated by the Solow growth model. Using sequential conditioning, it studies whether the convergence hypothesis implications can be shown to hold for the OECD economies. It finds that neither absolute nor conditional convergence, in the sense of economies approaching the OECD average, has taken place. The third chapter, "Cross Sectional Firm Dynamics: Theory and Empirical Results", extends ideas of distribution dynamics to a discrete choice setting, and extends the reasoning of Galton's Fallacy to the logit model. It provides evidence of the tendency of firm sizes to converge for the US chemicals sector by analysing dynamically evolving cross-section distributions. Finally, the fourth chapter, "Unemployment in Europe and Regional Labour Fluctuations" applies distribution dynamics ideas to a business cycle setting. It analyses the dynamics of employment for 51 European regions from 1960 to 1990, addressing the issue of whether regional shocks have aggregate effects on unemployment or the opposite. It uses a model for non-stationary evolving distributions to identify idiosyncratic and aggregate disturbances.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Business cycle fluctuations; Random fields