Nonmonotonic power for tests of a mean shift in a time series

By Ciprian M. Crainiceanu, Timothy J. Vogelsang. 2001

65 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/3707f556a387dc72e7e045c7a55946a377ed2a56

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Citations in the corpus

Strong Rules for Detecting the Number of Breaks in a Time Series (2003, background): “11 10 Crainiceanu and Vogelsang (2001) show that the use of data driven methods for the lag truncation parameter is the main source of non-monotonic power.”
Assessing the relative power of structural break tests using a framework based on the approximate Bahadur slope (2009, background): “As shown in Crainiceanu and Vogelsang (2001) and Deng and Perron (2008), α̂ = Op(||θ||(4)) and LT = Op(||θ||4/θT ) so that the bandwidth is increasing as the magnitude of the change increases.”
Dealing with Structural Breaks (2005, background): “Crainiceanu and Vogelsang (2001) also show how the problem is exacerbated when using estimates of the scale factor that allow for correlation, e.g., weighted sums of the autocovariance function.”
Tests for changing mean with monotonic power (2009, methodology): “Monte Carlo results of Crainiceanu and Vogelsang (2007) show that, in the presence of a changing mean, the value of the data dependent bandwidth will be very large, and nonmonotonic power can be found when such variance estimators are used.”
Testing the structural stability of temporally dependent functional observations and application to climate projections (2011, methodology): “Vogelsang [24]; Crainiceanu and Vogelsang [8]; Altissimo and Corradi [1]).”
Testing for Factor Loading Structural Change under Common Breaks (2015, methodology): “Crainiceanu and Vogelsang (2007) point out that the same problem occurs even in a model without lagged dependent variables, but the variances are estimated using a standard nonparametric heteroskedasticity and autocorrelation consistent (HAC) estimator.”
Testing for Multiple Structural Changes in Cointegrated Regression Models (2010, background): “Vogelsang (1999) and Crainiceanu and Vogelsang (2007) show that the power function of a wide variety of tests for a shift in the mean of a dynamic time series is non-monotonic with respect to the magnitude”
A Non-local Perspective on the Power Properties of the CUSUM and CUSUM of Squares Tests for Structural Change (2005, methodology): “If condition (5) holds, then ρ̂ (δ) p → 1 as ||δ|| increases such that C (δ) = Op(||δ||(4)), m = Op(||δ|| T ) and ω̂2WS = Op(||δ|| T ).”
Confidence Sets for the Date of a Single Break in Linear Time Series Regressions (2004, background): “The literature on estimation and construction of confidence sets for break dates goes back to Hinkley (1970), Hawkins (1977), Worsley (1979, 1986), Bhattacharya (1987) and others–see the reviews by Zacks (1983), Stock (1994) and Bhattacharya (1995) for additional discussion and references.”
Uniform change point tests in high dimension (2015, background): “, the power can decrease when the alternative gets farther away from the null); see, for instance, [18].”