Introduction

Change-point methods are useful for detecting parameter instabilities or changes in the moments of the data. The package provides tools for simultaneously detecting abrupt (i.e., breaks) or smooth change-points in any of the following features of a time series: mean, variance, covariance and autocorrelation.

Many change-point methods along with corresponding codes have been developed in the literature. Each method/code focuses on a specific feature of a time series and a specific type of break. All-Inside includes a single change-point procedure that is useful for any feature and any type of break. The user needs to just run a single code. That is why it is named All-Inside.

The methods are based on frequency domain statistics as developed in Casini and Perron (2021), “Change-Point Analysis of Time Series with Evolutionary Spectra”. The methods are also useful for the choice of subsamples for regression analyses.

Non-Technical Summary for Empirical Research

Download Non-Technical Summary here.

Software available in Matlab, R and Stata

• Matlab Package
• R Package (to be uploaded later)
• Stata Package (to be uploaded later)

Contributors

• Federico Belotti, University of Rome Tor Vergata.
• Alessandro Casini, University of Rome Tor Vergata.
• Leopoldo Catania, Aarhus University.
• Stefano Grassi, University of Rome Tor Vergata.
• Pierre Perron, Boston University.

Background Papers

• Belotti, F., A. Casini, L. Catania, S. Grassi and P. Perron, “Simultaneous Bandwidths Determination for DK-HAC Estimators and Long-Run Variance Estimation in Nonparametric Settings”. arXiv preprint arXiv:2103.00060.
• Casini, A. (2019), “Improved Methods for Statistical Inference in the Context of Various Types of Parameter Variation”. Ph.D Dissertation, Boston University.
• Casini, A. (2021), “Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models”. arXiv preprint arXiv:2103.02981.
• Casini, A. and P. Perron (2021), “Change-Point Analysis of Time Series with Evolutionary Spectra”. arXiv preprint arXiv:2106.02031.

Maintainer and Correspondence

• Alessandro Casini, University of Rome Tor Vergata.