In econometrics and statistics, a structural break is an unexpected change over time in the parameters of regression models, which can lead to huge forecasting errors and unreliability of the model in general.[1][2][3] This issue was popularised by David Hendry, who argued that lack of stability of coefficients frequently caused forecast failure, and therefore we must routinely test for structural stability. Structural stability − i.e., the time-invariance of regression coefficients − is a central issue in all applications of linear regression models.[4]
- ^ Antoch, Jaromír; Hanousek, Jan; Horváth, Lajos; Hušková, Marie; Wang, Shixuan (25 April 2018). "Structural breaks in panel data: Large number of panels and short length time series" (PDF). Econometric Reviews. 38 (7): 828–855. doi:10.1080/07474938.2018.1454378. S2CID 150379490.
Structural changes and model stability in panel data are of general concern in empirical economics and finance research. Model parameters are assumed to be stable over time if there is no reason to believe otherwise. It is well-known that various economic and political events can cause structural breaks in financial data. ... In both the statistics and econometrics literature we can find very many of papers related to the detection of changes and structural breaks.
- ^ Kruiniger, Hugo (December 2008). "Not So Fixed Effects: Correlated Structural Breaks in Panel Data" (PDF). IZA Institute of Labor Economics. pp. 1–33. Retrieved 20 February 2019.
- ^ Hansen, Bruce E (November 2001). "The New Econometrics of Structural Change: Dating Breaks in U.S. Labor Productivity". Journal of Economic Perspectives. 15 (4): 117–128. doi:10.1257/jep.15.4.117.
- ^ Ahmed, Mumtaz; Haider, Gulfam; Zaman, Asad (October 2016). "Detecting structural change with heteroskedasticity". Communications in Statistics – Theory and Methods. 46 (21): 10446–10455. doi:10.1080/03610926.2016.1235200. S2CID 126189844.
The hypothesis of structural stability that the regression coefficients do not change over time is central to all applications of linear regression models.