@article{andrews91b,
	author    = "Donald W. K. Andrews",
	title     = "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation",
	journal   = "Econometrica",
	volume   = "59",
	number   = "3",
	pages    = "817-858",
	year      = "1991",
	month    = "May",
}

@article{hidalgoseo13,
    title = "Testing for structural stability in the whole sample",
    journal = "Journal of Econometrics",
    volume = "175",
    number = "2",
    pages = "84 - 93",
    year = "2013",
    issn = "0304-4076",
    doi = "10.1016/j.jeconom.2013.02.008",
    url = "http://www.sciencedirect.com/science/article/pii/S0304407613000626",
    author = "Javier Hidalgo and Myung Hwan Seo",
    keywords = "Structural stability, GMM, Strong approximation, Extreme value distribution"
}

@unpublished{horvathricemiller19,
    author = "Gregory Rice and Curtis Miller and Lajos Horváth",
    title = "A new class of change point test of Rényi type",
    journal = "Journal of Business and Economic Statistics",
    note = "in-press"
}

@article{andrews03,
 ISSN = {00129682, 14680262},
 URL = {https://www.jstor.org/stable/1555535},
 abstract = {This paper considers tests for structural instability of short duration, such as at the end of the sample. The key feature of the testing problem is that the number, m, of observations in the period of potential change is relatively small--possibly as small as one. The well-known F test of Chow (1960) for this problem only applies in a linear regression model with normally distributed iid errors and strictly exogenous regressors, even when the total number of observations, n + m, is large. We generalize the F test to cover regression models with much more general error processes, regressors that are not strictly exogenous, and estimation by instrumental variables as well as least squares. In addition, we extend the F test to nonlinear models estimated by generalized method of moments and maximum likelihood. Asymptotic critical values that are valid as n → ∞ with m fixed are provided using a subsampling-like method. The results apply quite generally to processes that are strictly stationary and ergodic under the null hypothesis of no structural instability.},
 author = {D. W. K. Andrews},
 journal = {Econometrica},
 number = {6},
 pages = {1661--1694},
 publisher = {[Wiley, Econometric Society]},
 title = {End-of-Sample Instability Tests},
 volume = {71},
 year = {2003}
}

@article{ling07,
  author = "Ling, Shiqing",
  doi = "10.1214/009053606000001514",
  fjournal = "The Annals of Statistics",
  journal = "Ann. Statist.",
  month = "07",
  number = "3",
  pages = "1213--1237",
  publisher = "The Institute of Mathematical Statistics",
  title = "Testing for change points in time series models and limiting theorems for NED sequences",
  url = "http://dx.doi.org/10.1214/009053606000001514",
  volume = "35",
  year = "2007"
}

@article{famafrench15,
  title = "A five-factor asset pricing model",
  journal = "Journal of Financial Economics",
  volume = "116",
  number = "1",
  pages = "1 - 22",
  year = "2015",
  issn = "0304-405X",
  doi = "10.1016/j.jfineco.2014.10.010",
  url = "http://www.sciencedirect.com/science/article/pii/S0304405X14002323",
  author = "Eugene F. Fama and Kenneth R. French",
  keywords = "Asset pricing model, Factor model, Dividend discount model, Profitability, Investment"
}

@article{auehorvath13,
  author = {Aue, Alexander and Horváth, Lajos},
  title = {Structural breaks in time series},
  journal = {Journal of Time Series Analysis},
  year = {2013},
  volume = {34},
  number = {1},
  pages = {1-16},
  keywords = {Change-points, CUSUM, long memory, mean change, unit-root, variance change},
  doi = {10.1111/j.1467-9892.2012.00819.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9892.2012.00819.x},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9892.2012.00819.x},
  abstract = {This paper gives an account of some of the recent work on structural breaks in time series models. In particular, we show how procedures based on the popular cumulative sum, CUSUM, statistics can be modified to work also for data exhibiting serial dependence. Both structural breaks in the unconditional and conditional mean as well as in the variance and covariance/correlation structure are covered. CUSUM procedures are nonparametric by design. If the data allows for parametric modeling, we demonstrate how likelihood approaches may be utilized to recover structural breaks. The estimation of multiple structural breaks is discussed. Furthermore, we cover how one can disentangle structural breaks (in the mean and/or the variance) on one hand and long memory or unit roots on the other. Several new lines of research are briefly mentioned.}
}
