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预测分位数回归的新稳健推断方法AI 解读

This paper proposes a novel approach to offer a robust inferential theory across all types of persistent regressors in a predictive quantile regression model. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the…

含单整解释变量门限模型的稳健估计与推断AI 解读

This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is…

利用惩罚样条估计含非平稳解释变量的变系数模型AI 解读

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing the mixed model representation of penalized splines, we develop a likelihood ratio test statistic for checking the stability of the regression coefficients. We derive both the exact…

设定错误下多重结构突变模型断点估计量的一般一致性AI 解读

This paper considers the estimation of multiple-structural-break models under specification errors. A common example in economics is that the true model is measured in level, but a linear-log model is estimated. We show that, under specification errors, if there are more than one break points and if a single-break model is estimated, the estimated break point is consistent for one of the true break points. This consistency result applies to models with multiple regressors…