A bias-adjusted LM test of error cross-section independence

A New Diagnostic Test for Cross–Section Independence in Nonparametric Panel Data Model

A New Diagnostic Test for Cross–Section Independence in Nonparametric Panel Data Models

In this paper, we propose a new diagnostic test for residual cross–section independence in a nonparametric panel data model. The proposed nonparametric cross–section dependence (CD) test is a nonparametric counterpart of an existing parametric CD test proposed in Pesaren (2004) for the parametric case. We establish an asymptotic distribution of the proposed test statistic under the null hypothe...

A Nonlinear Panel Unit Root Test under Cross Section Dependence

We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-root processes against the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero proportion to be generated by unit root processes. The proposed test is simple to apply and accommodates cross section dependence. Monte Carlo sim...

Theoretical error of luminosity cross section at LEP

The aim of this note is to characterize briefly main components of theoretical error of the small angle Bhabha measurement at LEP and to discuss critically how solid these estimates really are, from todays perspective. We conclude that the existing theoretical error of the LEP luminometer process (small angle Bhabha) is rather solid, and we add some new discussion concerning the remaining uncer...

The Effect of Cognitive Bias Modification in the Attention Bias of Students With Test Anxiety

Abstract Introduction: Many of the students chr('39')educational problems, including academic failure and maladaptive behaviors, stem from exam anxiety caused by studentschr('39') cognitive bias. Cognitive bias modification can reduce attention biases students to be negative. Therefore, the purpose of the present study was to investigate the effectiveness of cognitive bias modification interven...