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单位根与协整检验的近似渐近分布函数AI 解读

Monte Carlo experiments and response surface regressions are used to calculate approximate asymptotic distribution functions for a number of well-known unit root and cointegration test statistics. These allow empirical workers to calculate approximate P values for these tests. The results of the paper are based on an extensive set of Monte Carlo experiments, which yield finite-sample quantiles for several sample sizes. Response surface regressions are then used to obtain…

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信息矩阵检验的一种新形式AI 解读

A new form of the information matrix test is developed for a wide variety of statistical models. The test is constructed against an explicit alternative with random parameter variation. It is computed using a double-length artificial regression instead of the more conventional outer-product-of-the-gradient regression, which is known to have very poor finite-sample properties. In Monte Carlo experiments for the case of univariate linear regression models, the new form…

基于辅助回归的模型设定检验AI 解读

Many specification tests can be computed with artificial linear regressions designed to be used as calculating devices to obtain test statistics and other quantities of interest. This article discusses the general principles that underlie all artificial regressions, and the use of such regressions to compute Lagrange multiplier and other specification tests based on estimates under the null hypothesis. The generality and power of artificial regressions as a means of…

利用辅助回归检验估计量的一致性AI 解读

We consider several issues related to Durbin-Wu-Hausman tests; that is, tests based on the comparison of two sets of parameter estimates. We first review a number of results about these tests in linear regression models, discuss what determines their power, and propose a simple way to improve power in certain cases. We then show how in a general nonlinear setting they may be computed as “score” tests by means of slightly modified versions of any artificial linear regression…

隐含备择与检验统计量的局部功效AI 解读

The local power of test statistics is analyzed by considering sequences of data-generating processes (DGPs) that approach the null hypothesis without necessarily satisfying the alternative. The three classical test statistics-LR, Wald, and LM-are shown to tend asymptot ically to the same random variable under all such sequences. The powe r of these statistics depends on the null, the alternative, and the sequence of DGPs in a geometrically intuitive way. This implies that,…

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存在备选假设时的若干模型设定检验AI 解读

Several procedures are proposed for testing the specification of an econometric model in the presence of one or more other models which purport to explain the same phenomenon.These procedures are shown to be closely related, but not identical, to the non-nested hypothesis tests recently proposed by Pesaran and Deaton [7], and to have similar asymptotic properties..They are remarkably simple both conceptually and computationally, and, unlike earlier techniques, they may be…

回归中的季节性：平滑先验的应用AI 解读

This article argues that conventional approaches to the treatment of seasonality in econometric investigation are often inappropriate. A more appropriate technique is to allow all regression coefficients to vary with the season, but to constrain them to do so in a smooth fashion. A Bayesian method of estimating smoothly varying seasonal coefficients is developed, based on Shiller's (1973) approach to estimating distributed lags. In a sampling experiment, this technique…

回归中的季节性：平滑先验的应用AI 解读

This article argues that conventional approaches to the treatment of seasonality in econometric investigation are often inappropriate. A more appropriate technique is to allow all regression coefficients to vary with the season, but to constrain them to do so in a smooth fashion. A Bayesian method of estimating smoothly varying seasonal coefficients is developed, based on Shiller's (1973) approach to estimating distributed lags. In a sampling experiment, this technique…

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自相关误差回归的极大似然估计程序AI 解读

The widely used Cochrane-Orcutt and Hildreth-Lu procedures for estimating the parameters of a linear regression model with first-order autocorrelation typically ignore the first observation. An alternative maximum likelihood procedure which incorporates the first observation and the stationarity condition of the error process is proposed in this paper. It is similar to the Cochrane-Orcutt procedure, and appears to be at least as computationally efficient. This estimator is…