
ModelFree Tests for Series Correlation in Multivariate Linear Regression
Testing for series correlation among error terms is a basic problem in l...
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A DFAbased bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
On the basis of detrended fluctuation analysis (DFA), we propose a new b...
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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 crosssec...
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Testing for exponentiality for stationary associated random variables
In this paper, we consider the problem of testing for exponentiality aga...
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Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
The GriponBerrou neural network (GBNN) is a recently invented recurrent...
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Can we disregard the whole model? Omnibus noninferiority testing for R^2 in multivariable linear regression and ^2 in ANOVA
Determining a lack of association between an outcome variable and a numb...
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Censored pairwise likelihoodbased tests for mixing coefficient of spatial maxmixture models
Maxmixture processes are defined as Z = max(aX, (1  a)Y) with X an as...
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Maxsum tests for crosssectional dependence of highdemensional panel data
We consider a testing problem for crosssectional dependence for highdimensional panel data, where the number of crosssectional units is potentially much larger than the number of observations. The crosssectional dependence is described through a linear regression model. We study three tests named the sum test, the max test and the maxsum test, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for sparse and nonsparse residuals in the linear regressions, respectively.And the maxsum test is devised to compromise both situations on the residuals. Indeed, our simulation shows that the maxsum test outperforms the previous two tests. This makes the maxsum test very useful in practice where sparsity or not for a set of data is usually vague. Towards the theoretical analysis of the three tests, we have settled two conjectures regarding the sum of squares of sample correlation coefficients asked by Pesaran (2004 and 2008). In addition, we establish the asymptotic theory for maxima of sample correlations coefficients appeared in the linear regression model for panel data, which is also the first successful attempt to our knowledge. To study the maxsum test, we create a novel method to show asymptotic independence between maxima and sums of dependent random variables. We expect the method itself is useful for other problems of this nature. Finally, an extensive simulation study as well as a case study are carried out. They demonstrate advantages of our proposed methods in terms of both empirical powers and robustness for residuals regardless of sparsity or not.
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