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空间样本选择模型估计：部分极大似然方法AI 解读

We study estimation of sample selection models with the spatially lagged latent dependent variable or spatial errors in both the selection and outcome equations under cross-sectional dependence. Since there is no estimation framework for the spatial-lag model and the existing estimators for the spatial-error model are computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator. We show that…

基于分位数的平滑转换风险价值估计AI 解读

Summary Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised autoregressive process governed by two parameter regimes with…

相关随机效应面板中不可分单指标模型的识别与估计AI 解读

The identification in a nonseparable single-index models with correlated random effects is considered in panel data with a fixed number of time periods. The identification assumption is based on the correlated random effects structure. Under this assumption, the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing. We suggest to linearly combine…

截断与删失回归模型的半参数稳健估计AI 解读

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but relatively imprecise. To improve its performance, we also propose data-adaptive and one-step trimmed estimators. We derive the…

一般修剪估计：非线性与受限因变量模型的稳健方法AI 解读

High-breakdown-point regression estimators protect against large errors and data contamination. We generalize the concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which renders robust estimators applicable far beyond the standard (non)linear regression models. We derive here the consistency and asymptotic distribution of the proposed general trimmed…