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计算动态乘数的计算机程序AI 解读

THE DYMULT (dynamic multipliers) program is designed to calculate impact, interim, and total multipliers of a simple linear simultaneous equations model with lags. Builders of an aggregate econometric model have often concentrated on the validity of signs and magnitudes of the estimated regression coefficients of each structural equation and neglected to check the stability conditions of the system, which could be a symptom of specification errors in the model. The DYMULT…

多元时间序列模型的参数估计AI 解读

Summary We shall present a practical iterative method for estimating parameters in mixed autoregressive moving-average models for multivariate stationary time series. This method essentially generalizes that of Marquardt (1963) as applied by Box and Jenkins (1970) to univariate time series. We shall investigate the statistical properties of the method in order to demonstrate desirable convergence behaviour.

线性回归模型中自相关误差的一种新检验AI 解读

Summary A test for autocorrelated errors in the linear model is introduced and shown to have, in general, greater power than the Durbin and Watson test for high values of autocorrelation.

序列相关观测下序列永久成分的测量AI 解读

Alternative systems of negative binomial and binomial weights are proposed for measuring the permanent component of a series as a weighted average of the observed series. If the observed series is generated by a stable first-order autoregressive process it is shown that there are alternative sets of weights using either weighting scheme for which the permanent and transitory components are uncorrelated.

滞后结构误设下分布滞后估计量的小样本性质AI 解读

The small sample properties of several distributed lag estimators are examined under conditions of misspecified lag structure, such as fitting a geometric estimator to a second order lag distribution. Evaluation includes analyses of the estimated average lags and the adjustment rates. Lag structure misspecification is found to yield large biases in the average lag and adjustment rate, especially compared to the biases resulting from a correctly specified but poorly designed…

使用三次样条的分段回归AI 解读

Spline theory and piecewise regression theory are integrated to provide a framework in which structural change is viewed as occurring in a smooth fashion. Specifically, structural change occurs at given points through jump discontinuities in the third derivative of a continuous piecewise cubic estimating function. Testing procedures are developed for detecting structural change as well as linear or quadratic segments. Finally, the techniques developed are illustrated…

具有向量自回归误差的似乎不相关回归估计AI 解读

We derive an asymptotically efficient method of estimating the coefficients of systems of seemingly unrelated regression equations when a first-order vector autoregressive error specification is hypothesized. This error specification is shown to contain several commonly used specifications as special cases. A Monte Carlo experiment was performed to examine the finite sample performance of our estimator in comparison with several other estimators. The general results were…

回归预测法与谱预测法的蒙特卡洛比较AI 解读

We consider the question of estimating the linear, least-squares predictor of the future values of a real-valued, discrete, purely nondeterministic, stationary time series from its known past. A method of estimating the prediction coefficients from the usual ‘windowed’ estimates of spectral density function is put forward and a Monte Carlo comparison of this method with the more usual regression method of finding the best fitting autoregressive process by recursively solving…

向量自回归误差下的似不相关回归估计AI 解读

We derive an asymptotically efficient method of estimating the coefficients of systems of seemingly unrelated regression equations when a first-order vector autoregressive error specification is hypothesized. This error specification is shown to contain several commonly used specifications as special cases. A Monte Carlo experiment was performed to examine the finite sample performance of our estimator in comparison with several other estimators. The general results were…

回归预测法与谱预测法的蒙特卡洛比较AI 解读

We consider the question of estimating the linear, least-squares predictor of the future values of a real-valued, discrete, purely nondeterministic, stationary time series from its known past. A method of estimating the prediction coefficients from the usual ‘windowed’ estimates of spectral density function is put forward and a Monte Carlo comparison of this method with the more usual regression method of finding the best fitting autoregressive process by recursively solving…

序列相关观测下序列永久成分的度量AI 解读

Alternative systems of negative binomial and binomial weights are proposed for measuring the permanent component of a series as a weighted average of the observed series. If the observed series is generated by a stable first-order autoregressive process it is shown that there are alternative sets of weights using either weighting scheme for which the permanent and transitory components are uncorrelated.

滞后结构误设下分布滞后估计量的小样本性质AI 解读

The small sample properties of several distributed lag estimators are examined under conditions of misspecified lag structure, such as fitting a geometric estimator to a second order lag distribution. Evaluation includes analyses of the estimated average lags and the adjustment rates. Lag structure misspecification is found to yield large biases in the average lag and adjustment rate, especially compared to the biases resulting from a correctly specified but poorly designed…

群时延的估计AI 解读

It is suggested that in estimating spectra and cross spectra for a pair of stationary series the estimation of the phase may be replaced by an estimate of the group delay, which is the derivative of phase with respect to frequency. An estimate is proposed and its asymptotic properties are investigated. The results are illustrated by simulation and by application to real data. The estimation of a group delay that is constant over a nondegenerate band is also discussed.

积分移动平均过程的样本自相关函数行为AI 解读

Little is known about the behaviour of the sample autocorrelation function when the generating stochastic process is nonstationary. In this note the behaviour of the sample autocorrelation function for a particular integrated moving average process is examined and the implications for model identification are discussed.

含滞后因变量和序列相关误差的回归模型中的序列相关检验AI 解读

The paper compares the power of two tests for serial correlation in regression models with lagged dependent variables, recently suggested by Durbin, with that of the likelihood ratio test by means of two sets of Monte-Carlo experiments-one in which the exogenous series is taken to be the quarterly GNP series for the USA and the other in which the exogenous series is generated by a known autoregression.

几何分布滞后参数的Liviatan估计量的抽样分布AI 解读

THE USE OF the geometric distributed lag in economics is widespread. The Liviatan [6] method for estimating its parameters is simple and provides consistent estimates. Moreover, it can be used to provide initial estimates for more sophisticated techniques [2]. Not much is known about the statistical properties of these estimators, especially their small sample properties. We do know that their asymptotic efficiencies are inferior to most alternatives [1], and recently Nagar…

自回归移动平均过程协方差矩阵的逆AI 解读

SUMMARY The problem of inverting the covariance matrix ZT of T successive observations from an autoregressive-moving average process of order (p, q) is discussed. The inverse is obtained for a moving average process (p = 0). An explicit expression is given for the elements of E-1 for a process of order (0, 2).

自回归移动平均过程协方差矩阵的逆AI 解读

The problem of inverting the covariance matrix ΣT of T sucoessive observations from an autoregressive-moving average process of order (p, q) is discussed. The inverse is obtained for a moving average process (p = 0). An explicit expression is given for the elements of Σ−1T for a process of order (0, 2).

具有依赖性的伯努利试验中的统计推断AI 解读

A model for Bernoulli trials with Markov dependence is developed which possesses the usual frequency parameter $p = P\lbrack X_i = 1\rbrack$ and an additional dependence parameter $\lambda = P\lbrack X_i = 1 \mid X_{i-1} = 1\rbrack$. Sufficient statistics for the model with $p$ and $\lambda$ unknown are found and an exact closed form expression for their small sample joint distribution is given. Large sample distribution theory is also given and small sample variances…

联合平稳高斯时间序列中循环二次型的近似AI 解读

Let $\{X(t), t = 0, \pm 1, \pm 2,\cdots\}$ be a $p$-dimensional, zero mean, stationary Gaussian time series with matrix-valued covariance function $R(u) = (r_{jk}(u)), u = 0, \pm 1, \pm 2, \cdots; j, k = 1, 2,\cdots, p$. Let $F(\omega)$ be the spectral density matrix of the time series (if $F(\omega)$ exists), and assume that $F(\omega)$ is positive definite for all $\omega\in(0, 2\pi\rbrack$. Let $\hat{F}_X(\omega_l)$ be an estimator of $F(\omega_l)$ formed by averaging $(2n + 1)$…

Box–Jenkins 传递函数—噪声模型的贝叶斯估计AI 解读

Summary The problem of estimating the coefficients of the transfer function-noise models proposed by Box and Jenkins is considered. A Bayesian approach is taken and the exact posterior density function of the coefficients is derived. Because of the complexity of this function, inference about individual parameters is virtually impossible except in the most simple cases. Accordingly, it is desirable to consider certain approximations. In particular, it is shown that inference…

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…

双变量非平稳过程的分析AI 解读

Summary In this paper, we propose a general definition of the evolutionary (time-dependent) cross-spectrum between two non-stationary processes and describe its physical interpretation. We also study the estimation of the evolutionary cross-spectrum at each time instant t from a single realization of a bivariate process. Further, we propose a definition (and a method of estimation) for the coherency (spectrum) between the two components of the bivariate process and show that…

具有线性结构的协方差矩阵的渐近有效估计AI 解读

One or more observations are made on a random vector, whose covariance matrix may be a linear combination of known symmetric matrices and whose mean vector may be a linear combination of known vectors; the coefficients of the linear combinations are unknown parameters to be estimated. Under the assumption of normality, equations are developed for the maximum likelihood estimates. These equations may be solved by an iterative method; in each step a set of linear equations is…