Multiple Structural Breaks in Cointegrating Regressions: A Model Selection Approach

2019 ◽  
Author(s):  
Alexander Schmidt ◽  
Karsten Schweikert
Keyword(s):  
Model Selection ◽  
Structural Breaks ◽  
Multiple Structural Breaks ◽  
Model Selection Approach ◽  
Selection Approach

Multiple structural breaks in cointegrating regressions: a model selection approach

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.

scholarly journals HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data

SoftwareX ◽  
2020 ◽  
Vol 12 ◽  
pp. 100559 ◽  
Author(s):  
Michael G. Leeming ◽  
Andrew P. Isaac ◽  
Luke Zappia ◽  
Richard A.J. O’Hair ◽  
William A. Donald ◽  
...  
Keyword(s):  
Model Selection ◽  
Drug Metabolites ◽  
Model Selection Approach ◽  
Selection Approach

scholarly journals Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 577
Author(s):  
Daisuke Murakami ◽  
Mami Kajita ◽  
Seiji Kajita
Keyword(s):  
Big Data ◽  
Model Selection ◽  
Regression Models ◽  
Large Scale ◽  
Spatial Regression ◽  
Varying Coefficients ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Spatial Regression Models ◽  
Spatially Varying

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.

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