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EN
Research background: Studying the dynamic characteristics of unemployment rate is crucial for both economic theory and macroeconomic policies. Despite numerous research, the empirical evidence about stochastic behaviour of the unemployment rate remains disputable. It has been widely agreed that most economic variables, including unemployment rates, are characterized by both structural breaks and nonlinearities. However, a little work is done to examine both features simultaneously. Purpose of the article: In this paper, we analyse the stationarity properties of unemployment rates of Euro area member countries. Also, we aim to test stochastic convergence of unemployment rates among member countries. Our empirical procedures explicitly allow for simultaneous gradual breaks and nonlinearities in the series. Methods: This paper develops a new unit root test procedure for panel data, allowing for both gradual structural breaks and asymmetric adjustment towards equilibrium. We carry out Monte Carlo simulations to examine small sample performance of the proposed test procedure and compare it to the existing test procedures. We apply the newly proposed test to examine the stochastic properties of the unemployment rates of Euro-member countries as well as relative unemployment rates vis-à-vis the Eurozone unemployment rate. Findings & value added: We find that the newly developed test procedure outperforms existing tests in highly nonlinear settings. Also, these tests reject the null hypothesis of unit root in more cases when compared to the existing tests. We find stationarity in the series only after allowing for structural breaks in the data generating process. Allowing for nonlinear and asymmetric adjustment in addition to gradual breaks provides evidence of stationarity in more cases. Furthermore, our results suggest that relative unemployment rate series are stationary, providing evidence in favour of stochastic convergence in unemployment rates. Overall, our results imply a limited room for coordinated economic policy to fight unemployment in the Eurozone.
PL
W artykule zaproponowano uogólnienie na przypadek wielowymiarowy dwóch twierdzeń, znanych dla zmiennych losowych jednowymiarowych, dotyczących zbieżności stochastycznej, czyli zbieżności według prawdopodobieństwa. Uogólnianymi twierdzeniami są słabe prawa wielkich liczb Markowa i Chinczyna. Wynika z nich, że przy odpowiednich założeniach ciąg średnich arytmetycznych wektorów losowych jest stochastycznie zbieżny do średniej arytmetycznej ich wartości oczekiwanych. W przeprowadzonych dowodach wykorzystano „łączne momenty rozkładów prawdopodobieństwa wektorów losowych” zaproponowane we wcześniejszych pracach autora. Opierają się one na definicji potęgi wektora w przestrzeni z iloczynem skalarnym.
EN
The paper presents a multidimensional generalisation (known for one-dimensional random variables) of two theorems regarding stochastic convergence – that is, convergence by probability. The generalised theorems are Markov’s and Chinchyn’s weak laws of great numbers. Both lead to the theory that, with the appropriate assumptions, a sequence of arithmetic averages of the random vectors converges their expected values to the arithmetic average. The proof for this thesis uses „whole moments of the multidimensional probability distribution”, which the author has proposed elsewhere. Their basis is a definition of the power of a vector in a space with a scalar product.
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