2018
DOI: 10.1080/1351847x.2018.1560346
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Market development and market efficiency: evidence based on nonlinear panel unit root tests

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Cited by 16 publications
(9 citation statements)
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“…However, in these countries, the potential role that the stock market has for economic development depends on the degree of stock market development. Stock market development, in turn, has been proven to have, a significant relationship with the degree of market efficiency, regardless of whether markets are developed, frontier or emergent, at least in the case of Europe [33]. Younger and less developed markets are usually found in the empirical literature to be less efficient than their more developed counterparts.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…However, in these countries, the potential role that the stock market has for economic development depends on the degree of stock market development. Stock market development, in turn, has been proven to have, a significant relationship with the degree of market efficiency, regardless of whether markets are developed, frontier or emergent, at least in the case of Europe [33]. Younger and less developed markets are usually found in the empirical literature to be less efficient than their more developed counterparts.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…However, none of these studies explore a holistic comparison and they touch only on some of the problems that we obtained from power analysis. In terms of empirical research, studies which utilize the tests discussed to make the correct unit root test in line with the results of the power study, as well as try to identify which one of the tests is the best, are [29][30][31][32][33]. These studies perform identification tests to determine whether the Fourier trend or the logistic trend fits the data better.…”
Section: The Behaviour Of the Fourier Function Under A Hybrid Dgp Witmentioning
confidence: 99%
“…In the works to test the random walk hypothesis, the statistical tests are widely applied to historical data, for example, the root test [6], run test [7], variance ratio test [8,9], multiple variance ratio test [10]. From a purely statistical viewpoint, these tests can be divided into the parametric such as autocorrelation test, and non-parametric such as run test.…”
Section: Introductionmentioning
confidence: 99%