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椭圆对称条件下动态非线性系统的半参数效率界AI 解读

We analyze semiparametric efficient estimation of non-linear simultaneous equation models in a time series context and derive a semiparametric efficiency bound for estimation of the parameters when the errors are i.i.d. from an unknown elliptically symmetric density. We derive the bound in the case of errors that are elliptically symmetric conditional on predetermined variables. Copyright Royal Economic Society 2007

椭圆对称下资本资产定价模型的有效检验：半参数方法AI 解读

Abstract We develop new tests of the capital asset pricing model that take account of and are valid under the assumption that the distribution generating returns is elliptically symmetric; this assumption is necessary and sufficient for the validity of the CAPM. Our test is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric, but otherwise unrestricted. The…

协整模型的自适应估计：模拟证据与远期外汇市场应用AI 解读

The paper reports simulation and empirical evidence on the finite-sample performance of adaptive estimators in cointegrated systems. Adaptive estimators are asymptotically efficient, even when the shape of the likelihood function is unknown. We consider two representations of cointegrated systems--triangular cointegrating regressions and error correction models. The motivation for and advantages of adaptive estimators in such systems are discussed and their construction is…

具有 ARMA 误差的协整回归自适应估计AI 解读

Adaptive maximum likelihood estimators are derived for the parameters of a cointegrating regression whose errors follow a stationary and invertible ARMA process with innovations of unknown distribution. It is shown how to use preliminary estimates of these innovations to nonparametrically estimate their density, which can in turn be used to construct an asymptotically efficient iterative estimator of the cointegrating vector. The asymptotic distribution of this estimator is…

误差修正模型的自适应估计AI 解读

This paper considers adaptive maximum likelihood estimation of reduced rank vector error correction models. It is shown that such models can be asymptotically efficiently estimated even in the absence of knowledge of the shape of the density function of the innovation sequence, provided that this density is symmetric. The construction of the estimator, involving the nonparametric kernel estimation of the unknown density using the residuals of a consistent preliminary…

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