Unemployment hysteresis in OECD countries: Centurial time series evidence with structural breaks

2008 ◽  
Vol 25 (2) ◽  
pp. 312-325 ◽  
Author(s):  
Chien-Chiang Lee ◽  
Chun-Ping Chang
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Oecd Countries

scholarly journals Is There A Stable Long-run Relationship Between Unemployment And Productivity?

2015 ◽  
Vol 18 (2) ◽  
pp. 57-75 ◽  
Author(s):  
João Tovar Jalles
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Oecd Countries ◽  
Econometric Methods ◽  
Long Run

This paper assesses whether productivity and unemployment have a stable long-run relationship. We explore a panel of 19 OECD countries between 1970 and 2012 and rely on recently developed time series econometric methods. Our findings suggest that unemployment and productivity are non-stationary in levels and in many individual cases these series are cointegrated, even after accounting for possible structural breaks. For many individual countries the long-run effect seems to be generally positive. There is also evidence of two-way causality, but the stronger directional relationship runs from unemployment to productivity.

scholarly journals Demand for money in the selected OECD countries: a time series panel data approach and structural breaks

2013 ◽  
Vol 45 (14) ◽  
pp. 1767-1776 ◽  
Author(s):  
Saten Kumar ◽  
Mamta B. Chowdhury ◽  
B. Bhaskara Rao
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Structural Breaks ◽  
Oecd Countries ◽  
Demand For Money

scholarly journals Are incomes converging among OECD countries? Time series evidence with two structural breaks

2004 ◽  
Vol 26 (1) ◽  
pp. 131-145 ◽  
Author(s):  
Mark C Strazicich ◽  
Junsoo Lee ◽  
Edward Day
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Oecd Countries

scholarly journals Examining GDP Growth and Its Volatility: An Episodic Approach

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 890
Author(s):  
Jakub Bartak ◽  
Łukasz Jabłoński ◽  
Agnieszka Jastrzębska
Keyword(s):  
Economic Growth ◽  
Genetic Algorithm ◽  
Time Series ◽  
Human Capital ◽  
Growth Rates ◽  
Structural Breaks ◽  
Negative Relationship ◽  
Gdp Per Capita ◽  
Gdp Growth ◽  
Per Capita

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

scholarly journals Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 262-279 ◽  
Author(s):  
Zhichao Guo ◽  
Yuanhua Feng ◽  
Thomas Gries
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Regression Models ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Middle Part ◽  
Change Points ◽  
Content Type ◽  
The World ◽  
Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

scholarly journals Structural Breaks and Explosive Behavior in the Long-Run: The Case of Australian Real House Prices, 1870–2020

2021 ◽  
Vol 15 (1) ◽  
pp. 72-84
Author(s):  
Vicente Esteve ◽  
Maria A. Prats
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Structural Breaks ◽  
House Prices ◽  
Unit Root Tests ◽  
Annual Data ◽  
Bubble Behavior ◽  
Long Period ◽  
Long Run ◽  
Long Time

Abstract In this article, we use tests of explosive behavior in real house prices with annual data for the case of Australia for the period 1870–2020. The main contribution of this paper is the use of very long time series. It is important to use longer span data because it offers more powerful econometric results. To detect episodes of potential explosive behavior in house prices over this long period, we use the recursive unit root tests for explosiveness proposed by Phillips et al. (2011), (2015a,b). According to the results, there is a clear speculative bubble behavior in real house prices between 1997 and 2020, speculative process that has not yet been adjusted.

Recovering cointegration via wavelets in the presence of non-linear patterns

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jorge Martínez Compains ◽  
Ignacio Rodríguez Carreño ◽  
Ramazan Gençay ◽  
Tommaso Trani ◽  
Daniel Ramos Vilardell
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gross National Product ◽  
Structural Breaks ◽  
Low Frequency ◽  
Cointegration Test ◽  
Seasonal Adjustment ◽  
Traditional Use ◽  
Monte Carlo Experiments ◽  
Non Linear

Abstract Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.

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