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动态面板同期相关的异方差稳健 Breusch–Pagan 检验AI 解读

This paper proposes a heteroskedasticity-robust Breusch–Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the…

GARCH 模型参数设定误检验的一阶渐近理论AI 解读

This paper develops a framework for the construction and analysis of parametric misspecification tests for generalized autoregressive conditional heteroskedastic (GARCH) models, based on first-order asymptotic theory. The principal finding is that estimation effects from the correct specification of the conditional mean (regression) function can be asymptotically nonnegligible. This implies that certain procedures, such as the asymmetry tests of Engle and Ng (1993, Journal…

个体效应 F 检验统计量的渐近分布AI 解读

(the number of cross-sections) and T is fixed (the number of time periods). Three theoretical results emerge: (i) the standard F-test procedure will still deliver asymptotically valid inferences; (ii) under (pure) local random effects, the F-test and random effects test procedures have identical asymptotic power; (iii) under local fixed, or random effects which are correlated with the regressors, the F-test will have higher asymptotic power than the random effects test.…

临时解雇与分群总体模型AI 解读

Abstract This paper develops and estimates a split population model for the duration of temporary layoffs in the German labour market; the population being split according to whether a layoff is temporary or permanent. A flexible piecewise constant hazard is employed and the recall propensity is used to condition the duration of a temporary layoff. The distinction between factors that influence the recall propensity and factors that influence the duration of a temporary…

控制线性回归模型预测误差检验的显著性水平AI 解读

This paper provides evidence on problems associated with using standard tests for predictive failure when the errors of a linear regression model are not normally distributed. The ability of a simple bootstrap procedure to give a useful degree of control over the significance levels is examined.