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动态面板模型中的初始条件与矩限制AI 解读

In this paper we consider estimation of the autoregressive error components model. When the autoregressive parameter is moderately large and the number of time series observations is moderately small, the usual Generalised Methods of Moments (GMM) estimator obtained after first differencing has been found to be poorly behaved. Here we consider alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. We…

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Jeffreys 先验下联立方程模型有限信息分析的后验分布AI 解读

This paper studies the use of the Jeffreys prior in Bayesian analysis of the simultaneous equations model (SEM). Exact representations are obtained for the posterior density of the structural coefficient β in canonical SEMs with two endogenous variables. For the general case with m endogenous variables and an unknown covariance matrix, the Laplace approximation is used to derive an analytic formula for the same posterior density. Both the exact and the approximate formulas…

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频域时间序列回归的高阶近似AI 解读

Second-order expansions and mean squared error approximations are given for efficient frequency domain regression estimators. While bandwidth choices do not figure in first order asymptotics for these estimators, they do influence second-order terms and it is shown how suitable choices will enhance second-order efficiency. Data-based bandwidth selection rules are given for practical implementation of these procedures. Two commonly used and asymptotically equivalent spectral…

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无限方差误差下的协整检验AI 解读

This paper develops the asymptotic theory for residual-based tests and quasi-likelihood ratio tests for cointegration under the assumption of infinite variance errors. This article extends the results of Phillips and Ouliaris (1990) and Johansen (1988, 1991) which are derived under the assumption of square-integrable errors. Here the limit laws are expressed in terms of functionals of symmetric stable laws rather than Brownian motion. Critical values of the residual-based…

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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…