Year: 2000
Type: article
Abstract: One of the important implications of real business cycle theory is that there should be\na positive relationship between economic fluctuations and economic growth, or, to be\nmore specific, that the growth of real gross domestic product per capita should fall as\nthe business cycle becomes less volatile. This is, of course, a conjecture that is open\nto empirical verification, at least in principle, but there are a variety of approaches that\nmay be taken which will not necessarily yield consistent sets of findings. Altman\n(1995), for example, uses primarily the Maddison (1991) data set of some 13\ncountries from 1870 to 1986 and detrends using log-linear trend lines across\nbenchmark years to obtain the cyclical component of output. He then uses rank and\nlinear correlation techniques to assess the strength of a possible positive relationship\nbetween cyclical volatility, measured as the standard deviation of the cyclical\ncomponent, and output growth across a variety of sub-periods.\nAltman finds little evidence in favour of this implication of real business\ncycles models, but it may be argued that his conclusion relies heavily on two\nquestionable features of his empirical approach. Both the method used to construct\nthe cyclical components and the correlation techniques employed to assess the\nstrength of any relationship between cyclical volatility and growth may be criticised\nas being less than statistical ‘best practice’. We therefore aim to reassess this\nevidence by extending Altman’s analysis in three directions. The first is to use a more\nextensive output per capita data set - we employ Maddison’s (1995) updated set of 22\ncountries, which now ends in 1994. We then employ several statistical techniques\nthat are explicitly designed to extract business cycle components from annual\neconomic time series and, finally, we use robust non-parametric methods to\ninvestigate the relationship between cyclical volatility and growth.
Author: Terence C. Mills
Language: English
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W325441004