异方差下条件处理效应的非参数估计及应用

处理效应模型作为分析政策效应的量化工具,在社会与经济的各个领域有着广泛的应用.现有文献为了得到处理效应的一致估计量,通常需要加一些较强的限制条件(如条件均值独立)或采用工具变量法,在较弱的与实际更吻合的条件下给出处理效应的一致估计量并不多见.本文在误差项对称的假定下,讨论了条件处理效应模型的非参数识别和估计,并进一步估计了平均处理效应.我们通过放松模型中函数形式的假定,同时考虑了较为普遍的广义异方差形式,大大减少了模型误设的可能性,拓展了现有模型的适用性.本文对估计量的大样本性质进行了分析,表明了估计量的一致性和渐近正态性,蒙特卡罗模拟显示了估计量良好的有限样本性质.最后,本文将估计量应用于研究大学教育回报及其性别差异,进一步解释了估计量的实用价值.     

异方差半参数变系数部分线性EV模型的经验似然

利用经验似然方法考虑异方差半参数变系数部分线性EV模型中兴趣参数置信域的构造.分别在误差方差已知和未知情形下,构造模型参数部分的经验对数似然比统计量,并验证非参数情形下的Wilks定理成立.模拟研究表明经验似然估计方法具有优良的性质.     

Forecasting high‐frequency financial data with the ARFIMA–ARCH model

Financial data series are often described as exhibiting two non‐standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA–ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid ( 1987 ). For daily data on the Swiss 1‐month Euromarket interest rate during the period 1986–1989, the ARFIMA–ARCH (5,d,2/4) model with non‐integer d is selected by AIC. Model‐based out‐of‐sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (τ > 40). Regarding volatility forecasts, however, the selected ARFIMA–ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd.     

异方差非简单样本的统计推断

就来自总体N(μ i,σ 2)的样本Y i=(y i 1,y i 2,…,y i mi) T,Yi～N mi(μ i1 i,σ 2N -1 i),N i=diag(n i 1,n i 2,…,ni m i),且Y 1,Y 2,…,Y k独立时,运用线性回归模型理论,给出了参数估计、假设检验、方差分析、多重比较的统计推断统计量.     

基于ARMA-EGARCH-M模型的沪深股市波动性分析

文章讨论了ARCH模型族的拟合波动性的优缺点,建立ARMA-EGARCH-M模型,简要说明了此模型的优点;以2000年1月11日-2006年3月15日上证综指和深证成指收盘价为样本,对我国沪深股市收益率分布用ARMA-EGARCH-M模型进行拟合分析,结果表明该模型能更有效地拟合我国沪深股市的波动性;最后解释实证结果和分析了我国股市的行为。     

异方差的检验比较和修正

异方差是计量经济工作中线性回归模型经常遇到的问题,异方差的存在对线性回归分析有很强的破坏作用.通过对异方差产生的原因和后果进行分析,利用异方差的戈德菲尔特-夸特检验、拉格朗日乘数(LM)检验、怀特检验方法,判断线性回归模型异方差的存在性.通过加权最小二乘法或可行广义最小二乘法进行修正,建立能够真正反映经济规律的经济模型,实现对经济的正确指导作用.     

Bias in the estimation of non‐linear transformations of the integrated variance of returns

Volatility models such as GARCH, although misspecified with respect to the data‐generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non‐linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non‐linear transformations of these forecasts will be biased estimators of the same non‐linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non‐linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non‐linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in‐sample and out‐of‐sample forecasts of three non‐linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.     

基于异方差高斯间距回归的产品设计时间预测模型

为了使产品设计时间预测模型既克服小样本和异方差噪音问题,又提供除预测值以外的其它有用信息,建立一种基于异方差高斯间距回归(heteroscedastic Gaussian margin regression,HGMR)的预测模型.首先,假定基于核函数的回归模型的权重向量服从高斯分布,以最小化相对熵为优化目标,利用预测值的置信区间设置约束条件,构造可同时给出预测值和预测区间的HGMR模型;然后,利用样本的独立性和异方差性对优化问题进行转化,证明HGMR模型具有较好的推广性能,并设计求解相应优化问题的迭代方法;最后,以注塑模具设计的实例进行分析,结果表明基于HGMR的时间预测模型是可行有效的.     

Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas

This paper investigates the implications of time‐varying betas in factor models for stock returns. It is shown that a single‐factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT‐AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT‐AR outperforms, in terms of in‐sample and out‐of‐sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH‐type models. Copyright © 2016 John Wiley & Sons, Ltd.