2020
DOI: 10.1108/aea-05-2020-0048
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What do unit root tests tell us about unemployment hysteresis in transition economies?

Abstract: Purpose This study aims to examine the hysteresis hypothesis in unemployment using monthly data from 13 countries in transition. Design/methodology/approach Stationarity in the unemployment rate of selected transition economies was analyzed using four different group unit root tests, namely, linear, structural breaks, non-linear and structural breaks and non-linear. Findings The empirical results show that the unemployment hysteresis hypothesis is valid for the majority of transition economies, including B… Show more

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Cited by 13 publications
(5 citation statements)
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“…Despite the widespread use of the augmented Dicky-Fuller and Phillips-Perron tests in standard unit root analysis, their reliability dwindles in smaller sample sizes, as underscored by Ali et al [102] and Khan et al [103]. To address this, our research adopted the KPSS unit root test, as endorsed by Ça glayan Akay et al [104] and Webb et al [105], ensuring a more reliable assessment of the stationarity of the variables in question. In the process of selecting the most suitable lag length for our model, we explored various options.…”
Section: Econometric Modelmentioning
confidence: 99%
“…Despite the widespread use of the augmented Dicky-Fuller and Phillips-Perron tests in standard unit root analysis, their reliability dwindles in smaller sample sizes, as underscored by Ali et al [102] and Khan et al [103]. To address this, our research adopted the KPSS unit root test, as endorsed by Ça glayan Akay et al [104] and Webb et al [105], ensuring a more reliable assessment of the stationarity of the variables in question. In the process of selecting the most suitable lag length for our model, we explored various options.…”
Section: Econometric Modelmentioning
confidence: 99%
“…According to Akay et al (2020), the unit root test calculates the specifications for a time series data set including deterministic, trend, nonlinear, and structural breaks. This test can make stronger and more precise estimates, making it possible to describe the structure of the data accurately.…”
Section: Wherementioning
confidence: 99%
“…Due to the characteristics of our data, we conducted a unit root test before conducting the multiple regression analysis to determine the impact of various SDGs on the GDP growth rate. Testing for unit roots is essential for determining if and how frequently time series data should be differentiated [167]. The unit-roots test is essential for determining the stationarity of the data in time series analysis [168][169][170].…”
Section: Unit Root Testmentioning
confidence: 99%