Forecasting Time Series Subject to Multiple Structural Breaks

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.

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Multiple structural breaks in cointegrating regressions: a model selection approach

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2020-0063 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Alexander Schmidt ◽

Karsten Schweikert

Keyword(s):

Structural Breaks ◽

Penalized Regression ◽

Adaptive Lasso ◽

Test Statistics ◽

New Approach ◽

Multiple Structural Breaks ◽

Long Run ◽

Model Selection Approach ◽

Regression Techniques ◽

Lasso Estimator

Abstract In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

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Shrinkage quantile regression for panel data with multiple structural breaks

Canadian Journal of Statistics ◽

10.1002/cjs.11659 ◽

2021 ◽

Author(s):

Liwen Zhang ◽

Zhoufan Zhu ◽

Xingdong Feng ◽

Yong He

Keyword(s):

Panel Data ◽

Quantile Regression ◽

Structural Breaks ◽

Multiple Structural Breaks

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Military expenditures and economic growth: allowing structural breaks in time series analysis in the case of India and Pakistan

Quality & Quantity ◽

10.1007/s11135-015-0217-x ◽

2015 ◽

Vol 50 (4) ◽

pp. 1487-1505 ◽

Cited By ~ 3

Author(s):

Abdul Jalil ◽

Hafiz Khuram Nadeem Abbasi ◽

Nazia Bibi

Keyword(s):

Economic Growth ◽

Time Series ◽

Time Series Analysis ◽

Structural Breaks ◽

Series Analysis ◽

Military Expenditures

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

China Agricultural Economic Review ◽

10.1108/caer-11-2013-0152 ◽

2015 ◽

Vol 7 (2) ◽

pp. 262-279 ◽

Cited By ~ 3

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.

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