本文提出圍繞一個(gè)非參數(shù)的定位功能，能夠檢測(cè)到一般的非參數(shù)的選擇條件分布對(duì)稱(chēng)測(cè)試。該測(cè)試是開(kāi)發(fā)一個(gè)通用串行依賴(lài)的背景下，創(chuàng)新的地方可能會(huì)出現(xiàn)一個(gè)未知的高階序列依賴(lài)結(jié)構(gòu)。檢驗(yàn)統(tǒng)計(jì)量是一種功能性的非參數(shù)的殘差和解釋變量的聯(lián)合經(jīng)驗(yàn)分布，其可以檢測(cè)非參數(shù)替代會(huì)聚到空的參數(shù)速率根-n的。臨界值估計(jì)用自舉技術(shù)容易實(shí)現(xiàn)的協(xié)助下，將所得測(cè)試的有效性的規(guī)律性的條件下正式有道理的。蒙特卡洛研究探討了測(cè)試的有限樣本性質(zhì)。我們還調(diào)查損失是否超過(guò)給定的使用我們的測(cè)試方法股市的可用信息收益的可能性較大。以下是在職研究生講座原文。

　　Testing Symmetry of a Nonparametric Conditional Distribution

　　This article proposes tests of symmetry of conditional distributions around a nonparametric location function able to detect general non-parametric alternatives. The test is developed in a general serial dependence context, where innovations may exhibit an unknown higher order serial dependence structure. The test statistic is a functional of the joint empirical distribution of non-parametric residuals and explanatory variables, which can detect non-parametric alternatives converging to the null at the parametric rate root-n. Critical values are estimated with the assistance of a bootstrap technique easy to implement, and the validity of the resulting test is formally justified under the regularity conditions. A Monte Carlo study examines the finite sample properties of the test. We also investigate whether losses are more likely than gains given the available information in stock markets using our testing approach.