Persistence in Convergence and Club Formation

Abstract

In this paper, we examine the convergence hypothesis using a long memory framework that allows for structural breaks and does not rely on a benchmark country using both univariate and multivariate estimates of the long memory parameter d. Using per capita GDP gaps, we confirm the findings of non‐stationarity and long memory behavior that have been found previously in the literature using univariate tests. However, the support for these findings is much weaker when using a multivariate framework, in which case we find more evidence of stationary behavior. Based on these results, we also investigate club formation, something that would suggest the presence of conditional convergence. We describe a club formation methodology using the sequential testing criteria that we have employed in our analysis as the basis for forming clusters or clubs of countries with similar convergence characteristics.

Dying together: A convergence analysis of fatalities during COVID-19

“…Depending on the outcome of the above tests, we can proceed to classify the different countries in clusters following the method introduced by Beylunioğlu et al (2017) and Özkan et al (2018) which combines pairwise testing with the maximal clique algorithm from computer science graph theory.…”

Health Behaviour Convergence: Evidence from Fractional (Long Memory) Convergence and British Microdata

“…The tests are applied in a sense that the analysis continues to apply them until it finds evidence in favor of some type of convergence, if there is any. Overall, the findings in Table illustrate the rejection frequencies of the tests defined above at the 5 per cent significance level based on critical values computed for T = 500 (Stengos et al ., ).…”

“…Therefore, the novelties of this paper stem not only from the fact that, to the best of our knowledge, this is the first attempt to empirically assess convergence issues in certain health measures in Britain, but also from the implementation of the new methodology of panel convergence testing, recommended by Stengos and Yazgan (2014a, 2014b) and Stengos et al . (). This particular methodological approach has the comparative advantage that considers the presence of potential breaks in the relationships under investigation, while it does not explicitly rely on a benchmark country/region to analyse the long memory behavior of the convergence hypothesis.…”

Trends and cycles in macro series: The case of US real GDP

“…where L stands for the lag operator (i.e., Lx t = x t-1 ); d can be any real value and u t is I(0) (defined as in footnote 1). Variants of this model have been used to analyze the behavior of GDP in various countries (see, e.g., Caporale & Skare, 2014;Dalkir, 2010;Ergemen, 2019;Mayoral, 2006;Michelacci & Zaffaroni, 2000;Stengos et al, 2018). Equation ( 4) is clearly more general than the classical stationary I(0) / nonstationary I(1) framework because it allows for a more cases, including (i) anti-persistence (d < 0); (ii) shortmemory or I(0) processes (d = 0); (iii) long-memory stationary processes (0 < d < 0.5); nonstationary mean-reverting processes (0.5 ≤ d < 1); (iv) unit roots (d = 1); and explosive patterns (d > 1).…”