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共同因子生成横截面依赖的高斯面板中渐近一致最强的单位根检验AI 解读

This paper considers testing for unit roots in Gaussian panels with cross-sectional dependence generated by common factors. Within our setup, we can analyze restricted versions of the two prevalent approaches in the literature, that of Moon and Perron (2004, Journal of Econometrics 122, 81–126), who specify a factor model for the innovations, and the PANIC setup proposed in Bai and Ng (2004, Econometrica 72, 1127–1177), who test common factors and idiosyncratic deviations…

渐近一致最强面板单位根检验：备择异质性的影响AI 解读

In a Gaussian, heterogeneous, cross-sectionally independent panel with incidental intercepts, Moon, Perron, and Phillips (2007, Journal of Econometrics 141, 416–459) present an asymptotic power envelope yielding an upper bound to the local asymptotic power of unit root tests. In case of homogeneous alternatives this envelope is known to be sharp, but this paper shows that it is not attainable for heterogeneous alternatives. Using limit experiment theory we derive a sharp…

半参数整数值 AR(p) 模型的自回归参数与创新分布有效估计AI 解读

Summary Integer-valued auto-regressive (INAR) processes have been introduced to model non-negative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of auto-regression coefficients and a probability distribution on the non-negative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to…

GARCH 模型中基于最大似然的估计量效率比较AI 解读

In this paper, we investigate the loss of asymptotic efficiency of semiparametric and quasi-maximum likelihood estimators relative to maximum likelihood estimators in models with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). For a general time-varying location-scale model, the factors that contribute to differences in efficiency among the estimators can be divided in two categories. One pertains to the parametric specifications of the conditional mean…

同时包含跳跃与条件异方差的模型：估计与检验AI 解读

In this article we develop a test for the hypothesis that a series (observed in discrete time) is generated by a diffusion process. This test is based on an overidentifying relation between variance and kurtosis parameters that holds for generalized autoregressive conditional heteroscedastic diffusions. The proposed test is not specific to a particular data frequency and clearly indicates the presence of jumps in dollar exchange rates. To assess the size and intensity of the…

摘要暂缺

时间序列模型中的自适应估计AI 解读

In a framework particularly suited for many time-series models we obtain a LAN result under quite natural and economical conditions. This enables us to construct adaptive estimators for (part of) the Euclidean parameter in these semiparametric models. Special attention is directed to group models in time series with the important subclass of models with time varying location and scale. Our set-up is confronted with the existing literature and, as examples, we reconsider…

摘要暂缺

GARCH 过程的时间聚合AI 解读

The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high…