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结构突变下的最优预测AI 解读

Summary This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator using only the…

时间序列条件方差的半参数估计量AI 解读

We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecification our estimator can…

非参数自相关误差下非参数回归的更有效估计AI 解读

We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial…

联合及条件数据生成过程与向量自回归的非参数时间序列估计AI 解读

In this paper we develop nonparametric estimators of the joint time series data generating process (DGP) of ( x t , y t ) at different t -values, of conditional DGP, of the conditional mean of x t given the past values of x and y , and, more generally, the conditional mean of ( x t , y t ) given their past values (vector autoregression). We establish, among other results, the central limit theorems for these estimators under far weaker mixing conditions than those used in…