A specification test for nonlinear nonstationary models
By Qiying Wang, Peter C. B. Phillips. 2012
80 citations recorded by the source corpus.
We provide a limit theory for a general class of kernel smoothed U-statistics that may be used for specification testing in time series regression with nonstationary data. The test framework allows for linear and nonlinear models with endogenous regressors that have autoregressive unit roots or near unit roots. The limit theory for the specification test depends on the self-intersection local time of a Gaussian process. A new weak convergence result is developed for certain partial sums of functions involving nonstationary time series that converges to the intersection local time process. This result is of independent interest and is useful in other applications. Simulations examine the finite sample performance of the test.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/cbb759e04a49b4c427be81bbcd9789701783b023
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In Statistics and Probability
Citations in the corpus
- Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, background): “…& Sun, 2001), weakly dependent time series data (e.g., Fan & Li, 1999; Li, 1999) and integrated time series data (e.g., Gao, King, Liu & Tjøstheim, 2009; Wang & Phillips, 2012; Sun, Cai & Li, 2015), to mention few among many contributions. tions to vary with the NOx concentration in the air.”
- Nonparametric Predictive Regression (2015, unknown): “In a related vein, Wang and Phillips (2012) found that nonparametric nonstationary specification tests have divergence rates under local alternatives that depend explicitly on the functional form andmay be inconsistent for certain functional forms.”