128 research outputs found
Nonparametric probability weighted empirical characteristic function and applications
No full textWe study certain properties of probability weighted quantities and suggest a nonparametric estimator of the probability weighted characteristic function introduced by Meintanis et al. (2014). Some properties of this estimator are studied and corresponding inference procedures for symmetry and the two-sample problem are proposed. Monte Carlo results on the finite-sample behavior of the procedures are also include
Validation tests for semi-parametric models
No full textTests are proposed for validation of the hypothesis that a partial linear regression model adequately describes
the structure of a given data set. The test statistics are formulated following the approach of Fourier-type
conditional expectations first suggested by Bierens [Consistent model specification tests. J Econometr.
1982;20:105–134]. The proposed procedures are computationally convenient, and under fairly mild conditions
lead to consistent tests. Corresponding bootstrap versions are compared with alternative procedures
for a wide selection of different estimators of the underlying partial linear mode
Comments on: Tests for multivariate normalit: a critical review with emphasis on weighted L2 -statistics
No full textWe discuss extension of the BHEP test to more general families of distribution
Rejoinder on: A review of testing procedures based on the empirical characteristic function
No full textGoodness-of-fit tests in conditional duration models
No full textWe propose specification tests for the innovation distribution in conditional duration models. The new tests are based either on the cumulative distribution function, or on exponential transforms such as the Laplace transform and the characteristic function, or on characterizations of the innovation-distribution under test. We study the finite-sample performance of the proposed procedures in comparison with alternative tests which employ nonparametric density estimates as well as with tests based on entropy. A bootstrap version of the tests is utilized in order to study the small sample behavior of the procedures. A real-data example illustrates the applicability of our method and confirms conclusions drawn by earlier author
Independence tests in semiparametric transformation models
No full textConsider an observed response Y which, following a certain transformation Yϑ by := Tϑ (Y ), can be expressed by a homoskedastic nonparametric regression model reference a vector X of regressors. If this transformation model is indeed valid then conditionally on X, the values of Yϑ may be viewed as being just location shifts of the regression error, for some value of the transformation parameter ϑ. We propose tests for the validity of this model, and establish the limiting distribution of the test statistics under the null hypothesis and under alternatives. Since the null distribution is complicated we also suggest a certain resampling procedure in order to approximate the critical values of the tests, and subsequently use this type of resampling in a Monte Carlo study of the finite-sample properties of the new tests. In estimating the model we rely on the methods proposed by Neumeyer, Noh and Van Keilegom (2016) for the aforementioned transformation model. Our tests however deviate from the tests suggested by Neumeyer et al. (2016) in that we employ an analogue of the test suggested by Hlávka, Hušková and Meintanis (2011) involving characteristic functions, rather than distribution function
Two-sample tests for multivariate functional data
No full textWe consider two–sample tests for functional data with observations which may be uni– or multi–dimensional. The new methods are formulated as L2–type criteria based on empirical characteristic functions and are convenient from the computational point of vie
Change-point methods for multivariate time-series: paired vectorial observations
No full textWe consider paired and two-sample break-detection procedures for vectorial observations and multivariate time series. The new methods involve L2-type criteria based on empirical characteristic functions and are easy to compute regardless of dimension. We obtain asymptotic results that allow for application of the methods to a wide range of settings involving on-line as well as retrospective circumstances with dependence between the two time series as well as with dependence within each series. In the ensuing Monte Carlo study the new detection methods are implemented by means of resampling procedures which are properly adapted to the type of data at hand, be it independent or paired, autoregressive or GARCH structured, medium or heavy-tailed. The new methods are also applied on a real dataset from the financial sector over a time period which includes the Brexit referendu
Testing serial independence with functional data
No full textWe consider tests of serial independence for a sequence of functional observations. The new methods are formulated as L2-type criteria based on empirical characteristic functions and are convenient from the computational point of view. We derive asymptotic normality of the proposed test statistics for both discretely and continuously observed functions. In a Monte Carlo study, we show that the new test is sensitive with respect to functional GARCH alternatives, investigate the choice of necessary tuning parameters, and demonstrate that critical values obtained by resampling lead to a test with good performance in any setup, whereas the asymptotic critical values may be recommended only for a sufficiently fine discretization grid. Finite-sample comparison with a distance (auto)covariance test criterion is also included, and the article concludes with application on a real data se
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