
A Powerful Portmanteau Test for Detecting Nonlinearity in Time Series
A new portmanteau test statistic is proposed for detecting nonlinearity ...
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Maxsum tests for crosssectional dependence of highdemensional panel data
We consider a testing problem for crosssectional dependence for highdi...
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Simultaneous Diagnostic Testing for LinearNonlinear Dependence in Time Series
Several goodnessoffit tests have been proposed to detect linearity in ...
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Determination, Calculation and Representation of the Upper and Lower Sealing Zones During Virtual Stenting of Aneurysms
In this contribution, a novel method for stent simulation in preoperativ...
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Conditional independence testing via weighted partial copulas
This paper introduces the weighted partial copula function for testing c...
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An Independence Test Based on Recurrence Rates. An empirical study and applications to real data
In this paper we propose several variants to perform the independence te...
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Stochastic Modeling of an Infectious Disease, Part IIIB: Analysis of the TimeNonhomogeneous BDI Process and Simulation Experiments of both BD and BDI Processes
In Section 1, we revisit the partial differential equation (PDE) for the...
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Extension of the Lagrange multiplier test for error crosssection independence to large panels with non normal errors
This paper reexamines the seminal Lagrange multiplier test for crosssection independence in a large panel model where both the number of crosssectional units n and the number of time series observations T can be large. The first contribution of the paper is an enlargement of the test with two extensions: firstly the new asymptotic normality is derived in a simultaneous limiting scheme where the two dimensions (n, T) tend to infinity with comparable magnitudes; second, the result is valid for general error distribution (not necessarily normal). The second contribution of the paper is a new test statistic based on the sum of the fourth powers of crosssection correlations from OLS residuals, instead of their squares used in the Lagrange multiplier statistic. This new test is generally more powerful, and the improvement is particularly visible against alternatives with weak or sparse crosssection dependence. Both simulation study and real data analysis are proposed to demonstrate the advantages of the enlarged Lagrange multiplier test and the power enhanced test in comparison with the existing procedures.
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