Strucchange: An R package for testing for structural change in linear regression models

By Achim Zeileis, Friedrich Leisch, Kurt Hornik, Christian Kleiber. 2002

1,171 citations recorded by the source corpus.

This paper reviews tests for structural change in linear regression models from the generalized fluctuation test framework as well as from the F test (Chow test) framework. It introduces a unified approach for implementing these tests and presents how these ideas have been realized in an R package called strucchange. Enhancing the standard significance test approach the package contains methods to fit, plot and test empirical fluctuation processes (like CUSUM, MOSUM and estimates-based processes) and to compute, plot and test sequences of F statistics with the supF , aveF and expF test. Thus, it makes powerful tools available to display information about structural changes in regression relationships and to assess their significance. Furthermore, it is described how incoming data can be monitored.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/fb17f3e663bf3df36e25100b0f7e878fec9874ce

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Citations in the corpus

A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals (2005, methodology): “…results in this paper were obtained using R 2.1.1 (R Development Core Team 2005, http://www.R-project.org/) and the package strucchange 1.2-11 (Zeileis et al., 2002), which are both freely available at no cost under the terms of the GNU General Public License (GPL) from the Comprehensive R…”
Beta Regression in R (2010, methodology): “While originally written for linear regression models (Zeileis, Leisch, Hornik, and Kleiber 2002), strucchange was extended by Zeileis (2006a) to compute generalized ﬂuctuation tests for structural change in models that are based on suitable estimating functions.”
ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages (2016, background): “, 2016), strucchange (Zeileis et al., 2002), and dlm (Petris, 2010), are popular choices for statisticians and researchers.”
Have jellyfish in the Irish Sea benefited from climate change and overfishing? (2011, methodology): “…packages used were: Flexibly reshape data (‘reshape’; Wickham, 2007), Testing for Structural Change in Linear Regression Models (‘strucchange’; Zeileis et al., 2002), Global validation of linear models assumptions (‘gvlma’; Pena & Slate, 2006), Testing Linear Regression Models (‘lmtest’;…”
ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013, methodology): “The strucchange package (Zeileis et al., 2002) provides a suite of tools for detecting changes within linear regression models.”
Change Points and Temporal Dependence in Reconstructions of Annual Temperature: Did Europe Experience a Little Ice Age? (2014, methodology): “ power: are they capable of detecting shifts in mean temperature of the sort that might have occurred during a European Little Ice Age? We will examine the power 7The older CUSUM test [implemented by Zeileis et al. (2002)] performed poorly in simulations, only detecting half as many breaks in short ”
Monitoring Structural Change in Dynamic Econometric Models (2002, methodology): “All computations and simulations have been performed in the statistical software package R (http://www.R-project.org/) and in particular the package strucchange (Zeileis, Leisch, Hornik, and Kleiber 2002).”
bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems (2007, methodology): “The RealInt dataset may be found in the strucchange package (Zeileis et al. 2002, 2003) and was examined in Bai and Perron (2003) and Garcia and Perron (1996).”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, methodology): “Using the monitoring software provided by the strucchange R package 68 , we construct control limits such that the false alarm rate is 0.05 in each of the control charts.”
segmented: An R package to Fit Regression Models with Broken-Line Relationships (2008, methodology): “The strucchange package by Zeileis et al. (2002) substantially is concerned with regression models having a different set of parameters for each ‘interval’ of the segmented variable, typically the time; strucchange performs breakpoint estimation via a dynamic grid search algorithm and allows for…”
A review of change point detection methods (2018, background): “wbsts [83,84] R cran.r-project. org/package= wbsts Change in 2nd order stationary structure [105] BinSeg [52,83] trend [113] R cran.r-project. org/package= trend Single shift in trend Opt strucchange [136] R cran.r-project. org/package= strucchange Shifts in mean and in linear model Opt SeqCBS [124]”