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Structural Breaks in Time Series

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  • Alessandro Casini
  • Pierre Perron

Abstract

This chapter covers methodological issues related to estimation, testing and computation for models involving structural changes. Our aim is to review developments as they relate to econometric applications based on linear models. Substantial advances have been made to cover models at a level of generality that allow a host of interesting practical applications. These include models with general stationary regressors and errors that can exhibit temporal dependence and heteroskedasticity, models with trending variables and possible unit roots and cointegrated models, among others. Advances have been made pertaining to computational aspects of constructing estimates, their limit distributions, tests for structural changes, and methods to determine the number of changes present. A variety of topics are covered. The first part summarizes and updates developments described in an earlier review, Perron (2006), with the exposition following heavily that of Perron (2008). Additions are included for recent developments: testing for common breaks, models with endogenous regressors (emphasizing that simply using least-squares is preferable over instrumental variables methods), quantile regressions, methods based on Lasso, panel data models, testing for changes in forecast accuracy, factors models and methods of inference based on a continuous records asymptotic framework. Our focus is on the so-called off-line methods whereby one wants to retrospectively test for breaks in a given sample of data and form confidence intervals about the break dates. The aim is to provide the readers with an overview of methods that are of direct usefulness in practice as opposed to issues that are mostly of theoretical interest.

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  • Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
  • Handle: RePEc:arx:papers:1805.03807
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    2. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2021. "Efficient Combined Estimation under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202107, University of Kansas, Department of Economics, revised Jan 2021.
    3. Peter Boswijk & Giuseppe Cavaliere & Iliyan Georgiev & Anders Rahbek, 2019. "Bootstrapping Non-Stationary Stochastic Volatility," Tinbergen Institute Discussion Papers 19-083/III, Tinbergen Institute.
    4. Pierre Perron & Yohei Yamamoto, 2021. "Testing for Changes in Forecasting Performance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 148-165, January.
    5. Pierre Perron & Yohei Yamamoto & Jing Zhou, 2020. "Testing jointly for structural changes in the error variance and coefficients of a linear regression model," Quantitative Economics, Econometric Society, vol. 11(3), pages 1019-1057, July.
    6. Prados de la Escosura, Leandro & Rodríguez-Caballero, Carlos Vladimir, 2020. "Growth, War, and Pandemics: Europe in the Very Long-run," CEPR Discussion Papers 14816, C.E.P.R. Discussion Papers.
    7. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org.
    8. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    9. Federico Belotti & Alessandro Casini & Leopoldo Catania & Stefano Grassi & Pierre Perron, 2021. "Simultaneous Bandwidths Determination for DK-HAC Estimators and Long-Run Variance Estimation in Nonparametric Settings," Papers 2103.00060, arXiv.org.
    10. Denny IRAWAN & OKIMOTO Tatsuyoshi, 2020. "Overinvestment and Macroeconomic Uncertainty: Evidence from Renewable and Non-Renewable Resource Firms," Discussion papers 20059, Research Institute of Economy, Trade and Industry (RIETI).
    11. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org.
    12. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    13. Kashif Yousuf & Serena Ng, 2019. "Boosting High Dimensional Predictive Regressions with Time Varying Parameters," Papers 1910.03109, arXiv.org.
    14. Szybisz, Martin Andres, 2018. "Banking net income and macroeconomics, from multicollinearity to Granger causality using US data," MPRA Paper 90473, University Library of Munich, Germany.
    15. Alessandro Casini & Pierre Perron, 2021. "Minimax MSE Bounds and Nonlinear VAR Prewhitening for Long-Run Variance Estimation Under Nonstationarity," Papers 2103.02235, arXiv.org.

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    5. Seong Yeon Chang & Pierre Perron, 2016. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 555-574, July.
    6. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
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    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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