Testing the hysteresis effect in the US state-level unemployment series

Abstract

This paper re-examines the stochastic time series behaviour of the monthly unemployment rate in 50 states of the United States (US) for the period 1976-2017 using a number of state-of-the-art unit root tests. The new developments incorporate structural break, nonlinearity, asymmetry, and cross-sectional correlation within panel-data estimation including the use of a sequential panel selection method. While not previously considered, sequential panel selection enabled us to determine and separate the stationary and nonstationary series in the sample. The empirical findings are in support of the stationarity of unemployment rate in 47 states. The findings confirm a natural rate hypothesis for the labour markets in the most US states, indicating that labour market shocks have solely temporary effects on state-level unemployment. This empirical study provides significant state-specific policy implications.

Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth

“…Moreover, OEHa (2018) test states the same results, whereas OY (2014) reveals stationarity for all variables. Our results are consistent with Omay et al [ 48 ], who documented that future movements in macroeconomic variables may be predicted based on previous behavior, because time series data on the state level follow mean-reversing processes. To conclude, all the variables that will be included in machine learning models were found to be stationary.…”

“…According to Omay et al [ 48 ], these nonlinearities may emerge simultaneously in the data generating process to identify these hybrid structures (Table 4 ). OSHa unit root test provides evidence of stationarity for seven stationary variables, except GCF.…”

Danimarka ve İsveç İçin İşsizlik Histerisi Hipotezinin Test Edilmesi

“…The study of Blanchard and Summers (1986) is at the forefront of the empirical studies on unemployment hysteresis. Apart from this study, other examined empirical studies are as follows: Brunello (1990), Neudorfer et al (1990), Jaeger and Parkinson (1994), Røed (1996), Song and Wu (1997), Arestis and Mariscal (1999), Papell et al (2000), León-Ledesma (2002), Camarero and Tamarit (2004), Camarero et al (2006), Gustavsson and Österholm (2006), Camarero et al (2008), Gomes and Da Silva (2008), Lee and Chang (2008), Lee et al (2009), Lee (2010), Chang (2011), Ayala et al (2012), Cevik and Dibooglu (2013), Lee et al (2013), Bakas and Papapetrou (2014), Cheng et al (2014), Furuoka (2014), Tiwari (2014), Jiang and Chang (2016), Akdoğan (2017), Güriş et al (2017), Meng et al (2017), Bahmani-Oskooee et al (2018), Rodriguez-Gil (2018), Yaya et al (2019), Khraief et al (2020), Omay et al (2020), Yilanci et al (2020), Omay et al (2021), Bostancı and Koç (2022), Caporale et al (2022),…”

Smooth Break Detection and De-Trending in Unit Root Testing

“…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.…”