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A restriction on a semiparametric or nonparametric econometric time series model determines the value of a finite-dimensional functional τ of an infinite-dimensional nuisance function. The estimate of τ and its estimated covariance matrix use nonparametric probability and spectral density estimation. A consequent test of the restriction is given approximate large sample justification under absolute regularity on the time series and other conditions. The methodology relates…

根号 N 一致的半参数回归AI 解读

One type of semiparametric regression is b8X A u(Z), where b and u(Z) are an unknown slope coefficient vector and function. Estimates of b based on incorrect parametrization of u are generally inconsist ent, whereas consistent nonparametric estimates converge slowly. An e stimate, bC, is constructed by inserting nonpar-ametric regression es timates in the nonlinear orthogonal projection on Z. Under regularity conditions bC is shown to be N1/2-consistent for b and…

计量统计量之间的随机差异AI 解读

In a somewhat general context, the author calculate s the order in probability of the difference between consistent roots of rival estimating equations, with application to point estimates i n parametric and nonparametric models, interval estimates, and test s tatistics. Differences in the statistical performances of various com monly-used iterative procedures are detected. He discusses implicatio ns of his results for higher-order efficiency comparisons and for mat ching…

半参数计量经济学：综述AI 解读

Abstract Semiparametric econometric models contain both parametric and nonparametric components, reflecting in some fashion what has been learned from economic theory and previous empirical experience, and what remains unknown. They raise such questions as how well the parametric component can be estimated, and how to construct rules of inference with good statistical properties. The paper attempts to survey the econometric and most relevant statistical literature on…

未知形式异方差下的渐近有效估计AI 解读

In a multiple-regression model the residual variance is an unknown function of the explanatory variables, and estimated by nearest-neighbor nonparametric regression. The resulting weighted least-squares estimator of the regression coefficients is shown to be adaptive, in the sense of having the same asymptotic distribution, to first order, as estimators based on knowledge of the actual variance function or a finite parameterization of it. A similar result was established by…

对 Singh 与 Ullah 论文的评论AI 解读

It is shown that the words ‘far weaker’ used four times by Singh and Ullafa [4] in describing their mixing condition in relation to ones of Robinson [1] for establishing the central limit theorem for nonparametric estimators, should in each case be replaced by theword ‘stronger,’ Singh and Ullah's [4] condition being equivalent to complete independence across time.

存在缺失观测时的回归序列相关检验AI 解读

SUMMARY In order to test for serial correlation in residuals for static time series regression in the presence of missing data, the score principle is applied both to the likelihood conditional on the observation times, and to an unconditional form of likelihood. Asymptotic distributions of the test statistics are established, under both the null hypothesis of no serial correlation, and sequences of local, correlated, alternatives, enabling analytic comparison of efficiency.

混合分布时间序列的分析AI 解读

Some stationary and non-stationary time series arise from mixed distributions, the probabilities attached to the occurrence of certain values being positive, while a continuum of possible values is also involved. Such series are modeled in terms of a stationary Gaussian process $X_t$, which is censored when it crosses certain thresholds. Procedures are proposed for estimating the autocorrelation function of $X_t$. Their strong consistency and asymptotic normality are…

存在缺失数据时的时间序列模型估计AI 解读

A method is proposed for the estimation of models for discrete time series in the presence of missing data. Some justification is given for the use of this method over alternatives; the choice of estimator is likely to be governed by the pattern of missing data, the nature of the time series model, and computational considerations. The method's performance in estimating simple models is studied by simulations, and it is applied to a time series of pollution levels containing…

线性时不变系统的分布滞后近似AI 解读

An infinite distributed lag system is approximated by a truncated one, which can be consistently estimated. We investigate the rate at which the parameter space in the approximate model can be increased as sample size increases, in such a way that these estimates will provide consistent estimates of the underlying system.

时间序列分析中的一致性AI 解读

A number of statistics that arise in time series analysis can be represented as the sum of a partial realization of a possibly serially dependent and nonstationary discrete-parameter stochastic process. The almost sure and $L_p, p > 1$, convergence of such statistics is investigated, under various moment conditions. The results are applied to the least squares estimates of multiple regressions.

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微分方程参数估计中的混叠问题AI 解读

Summary This paper is concerned with the estimation of the parameters in a simple open-loop differential equation model. We suggest first some frequency-domain “narrow-band” and “broad-band” estimators that are based on the assumption that the observable input variable has no spectral mass beyond Nyquist frequency. We study the asymptotic biases of these estimators under the contradictory hypothesis that the spectral density of the input is a rational function of frequency.…

使用离散数据的连续时间回归AI 解读

A general continuous time distributed lag model is considered. The problem is that of estimating the parameters of the kernel when, as is often the case, the available data consist not of a continuous record but of discrete observations recorded at regular intervals of time. Fourier transformation of the model and insertion of the computable, discrete Fourier transforms of the variables produce an approximate model which is of non-linear regression type and is relatively…

未知滞后长度的滞后回归AI 解读

Summary The problem considered is that of determining the parameters in a lagged regression of one stationary time series on another when the lag is unknown and is not an integral multiple of the time interval between observations. The residual in the regression is also taken to be a stationary time series. The method of estimation is based on the maximization with respect to the lag of a form of autocorrelation between the two series. However, the autocorrelation is defined…