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A great deal of applied economic work relies upon empirical estimates of the correlation matrix. However due to the finite size of both the number of variables and the number of observations, a reliable determination of the correlation matrix may prove to be problematic. The structure of the correlation matrix may be dominated by noise rather than by true information. Random matrix theory was developed in physics to overcome this problem, and to enable true information in a matrix to be distinguished from noise. There is now a large literature in which it is applied successfully to financial markets and in particular to portfolio selection. The author illustrates the application of the technique to macro-economic time-series data. Specifically, the evolution of the convergence of the business cycle between the capitalist economies from the late 19th century to 2006. The results are not in sharp contrast with those in the literature obtained using approaches with which economists are more familiar. However, there are differences, which RMT enables us to clarify."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2008"}}]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Random matrix theory"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Macroeconomic time-series data"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"International business cycle"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Synchronisation"}}]},{"typeName":"topicClassification","multiple":true,"typeClass":"compound","value":[{"topicClassValue":{"typeName":"topicClassValue","multiple":false,"typeClass":"primitive","value":"C69"},"topicClassVocab":{"typeName":"topicClassVocab","multiple":false,"typeClass":"primitive","value":"JEL"}},{"topicClassValue":{"typeName":"topicClassValue","multiple":false,"typeClass":"primitive","value":"E32"},"topicClassVocab":{"typeName":"topicClassVocab","multiple":false,"typeClass":"primitive","value":"JEL"}},{"topicClassValue":{"typeName":"topicClassValue","multiple":false,"typeClass":"primitive","value":"N10"},"topicClassVocab":{"typeName":"topicClassVocab","multiple":false,"typeClass":"primitive","value":"JEL"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Paul Ormerod (2008). Random Matrix Theory and Macro-Economic Time-Series: An Illustration Using the Evolution of Business Cycle Synchronisation, 1886–2006. Economics: The Open-Access, Open-Assessment E-Journal, Vol. 2, 2008-26. <a href=\"http://www.economics-ejournal.org/economics/journalarticles/2008-26\">  http://www.economics-ejournal.org/economics/journalarticles/2008-26</a>"}}]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2008"},{"typeName":"distributor","multiple":true,"typeClass":"compound","value":[{"distributorName":{"typeName":"distributorName","multiple":false,"typeClass":"primitive","value":"Economics: The Open-Access, Open-Assessment E-Journal"},"distributorURL":{"typeName":"distributorURL","multiple":false,"typeClass":"primitive","value":"http://www.economics-ejournal.org"},"distributorLogoURL":{"typeName":"distributorLogoURL","multiple":false,"typeClass":"primitive","value":"http://www.economics-ejournal.org/logo.png"}}]},{"typeName":"distributionDate","multiple":false,"typeClass":"primitive","value":"2008"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2009-10-19"},{"typeName":"timePeriodCovered","multiple":true,"typeClass":"compound","value":[{"timePeriodCoveredStart":{"typeName":"timePeriodCoveredStart","multiple":false,"typeClass":"primitive","value":"1871"},"timePeriodCoveredEnd":{"typeName":"timePeriodCoveredEnd","multiple":false,"typeClass":"primitive","value":"2006"}}]},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["aggregate data"]}]},"geospatial":{"displayName":"Geospatial Metadata","name":"geospatial","fields":[{"typeName":"geographicCoverage","multiple":true,"typeClass":"compound","value":[{"otherGeographicCoverage":{"typeName":"otherGeographicCoverage","multiple":false,"typeClass":"primitive","value":"Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, the United States"}}]}]},"socialscience":{"displayName":"Social Science and Humanities Metadata","name":"socialscience","fields":[{"typeName":"timeMethod","multiple":false,"typeClass":"primitive","value":"Time series"},{"typeName":"frequencyOfDataCollection","multiple":false,"typeClass":"primitive","value":"yearly"}]}},"files":[{"description":"aggregate data in xls file ","label":"OUTPUTPC07A.xls","restricted":false,"version":1,"datasetVersionId":51662,"categories":["2. 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