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用于非嵌套假设检验的工具变量统计量的渐近性质AI 解读

This paper develops Instrumental Variables statistics for testing non-nested hypotheses when the hypotheses considered are single equations from a system of linear dynamic simultaneous equations. Asymptotic distributions of those statistics and of comparable Maximum Likelihood statistics are derived under the null hypothesis, a local non-nested alternative hypothesis, and a local "comprehensive" alternative hypothesis. The asymptotic powers of the non-nested hypothesis tests…

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用于时间序列分配的随机游走—马尔可夫模型AI 解读

This article describes a technique for distributing quarterly time series across monthly values. The method generalizes an approach described by Fernández (1981). The article also presents results of a test of the accuracy of these two approaches and of the accuracy of two standard procedures suggested by Chow and Lin (1971).

用一组标量偏自相关系数刻画协方差矩阵AI 解读

It has been shown that the autocovariance matrices of a stationary multivariate time series can be uniquely characterized by a sequence of the normalized partial autocorrelation matrices having singular values less than one. In this note, we show that the same autocovariance matrices can be also uniquely characterized by a set of sequences of scalar partial autocorrelation coefficients whose magnitudes are all less than one.

一阶移动平均参数最小二乘估计的一致性AI 解读

The consistency of the least squares estimator of the parameter of the first order moving average time series is proven for the parameter in the interval $\\lbrack -1, 1 \\rbrack$.

高斯 ARMA 过程估计量的二阶渐近效率AI 解读

In this paper we investigate an optimal property of maximum likelihood and quasi-maximum likelihood estimators of Gaussian autoregressive moving average processes by the second order approximation of the sampling distribution. It is shown that appropriate modifications of these estimators for Gaussian ARMA processes are second order asymptotically efficient if efficiency is measured by the degree of concentration of the sampling distribution up to second order. This concept…

利用汇总数据估计马尔可夫概率模型参数AI 解读

In this paper we consider the problem of estimating the parameters of a Markov model using so-called macro data. It will be shown that the stochastic process of the macro data is a Markov chain, which uniquely determines the probability structure of the underlying Markov model. A conditional least squares estimator exists under very weak conditions and this estimator is strongly consistent as time tends to infinity. Moreover this estimator is shown to be asymptotically…

确定时间序列周期的一种方法AI 解读

The periods corresponding to peaks in the autoregressive and window spectral density estimators are shown to be consistent and asymptotically normal estimators of peak periods in the true spectral density under assumptions of known autoregressive order and under slight modifications of the assumptions often made to show the asymptotic normality of window estimators, respectively. The asymptotic variances are obtained, and the use of the theory is illustrated by obtaining an…

“真实”时间序列及其指标AI 解读

A problem that economic statisticians frequently face is to estimate the true movement in a time series on the basis of two or more imperfect indicators. A well-known example is nonagricultural employment as indicated (a) by a monthly survey of households and (b) by a monthly survey of employers. This article describes a procedure for estimating true changes in a time series as a linear combination of two indicators, with weights for indicators chosen so as to minimize…

人口预测的准确性AI 解读

Population projections are key elements of many planning and policy studies but are inherently inaccurate. This study of past population projection errors provides a means for constructing confidence intervals for future projections. We first define a statistic to measure projection errors independently of the size of the population and the length of the projection period. A sample of U.S. Census Bureau and United Nations projections indicates that the distributions of…

确定时间序列周期的一种方法AI 解读

The periods corresponding to peaks in the autoregressive and window spectral density estimators are shown to be consistent and asymptotically normal estimators of peak periods in the true spectral density under assumptions of known autoregressive order and under slight modifications of the assumptions often made to show the asymptotic normality of window estimators, respectively. The asymptotic variances are obtained, and the use of the theory is illustrated by obtaining an…

有理谱密度模型中混叠问题的维度AI 解读

This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It focuses on rational specifications of spectral density matrices since rational parameterizations are commonly employed in the analysis of the time series data.

非线性自回归型时间序列模型的递归估计AI 解读

SUMMARY An adaptive estimation principle is considered for non-standard time series models; non-linear autoregressive processes in one dimension. Because of the recursive nature of the resulting estimator, it is computationally appealing, especially when a time series is considered as a flow of data. The least squares estimator is, for example, quite likely to be inferior. Asymptotic properties are derived under fairly weak assumptions, and special applications are…

检验最小二乘回归残差是否由高斯随机游走生成AI 解读

This paper considers the null hypothesis that the errors on a regression equation form a random walk. By using the standard Durbin-Watson assumptions, we derive three test statistics that are uniformly most powerful against the alternative hypothesis that the errors are being generated by the stationary first order Markoff process. Unfortunately, the tabulated lower and upper bounds are too wide apart and so we compare the powers of the three tests using simulated as well as…

高阶连续时间动态模型结构参数的高斯估计AI 解读

This paper is concerned with the efficient estimation of structural parameters in closed linear systems of higher order stochastic differential equations when the data are in discrete form and the model generally includes both stock and flow variables. A general existence and uniqueness theorem for the solution of such a system is proved and used in the rigorous derivation of exact discrete models satisfied by the various types of data. It is shown how these models can be…

非线性时间序列预测的一项应用AI 解读

By means of a real application, it is seen how ARIMA forecasts can be improved when nonlinearities are present. The autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption. Once nonlinearity has been detected, parsimonious bilinear processes seem rather adequate to model it. The detection of nonlinearity and the forecast improvement appear to be rather robust with respect to changes in the linear and bilinear…

货币总量的季节调整：美联储委员会报告概要AI 解读

The Committee of Experts on Seasonal Adjustment Techniques was formed by the Federal Reserve Board to examine and make recommendations concerning the Board's procedures for seasonally adjusting the money supply and related series. This article summarizes the report of that committee.

多元时间序列模型中永久与暂时影响的分析AI 解读

Several multiple time series models are developed and applied to the analysis and forecasting of the M1 and M2 money supply aggregates. These models feature a decomposition of the time series into permanent and transient influences or components. This decomposition appears to enhance forecasting accuracy and is associated with a variance-covariance allocation parameter that is also estimated from the data. Conditional maximum likelihood estimates for model parameters are…

多元时间序列建模与扩展样本互相关AI 解读

This article provides an expository account of the multivariate autoregressive moving average models and proposes an extended sample cross-correlation approach for practical model identification. An iterative model building procedure for applying these models to real data is discussed and demonstrated by analyzing the 5-series U.S. Hog Data.

连续时间延迟自回归过程札记AI 解读

A delayed autoregressive model in continuous time is proposed and its covariance structure analysed. A nonidentifiability problem in parameter estimation is pointed out.

部分数据缺失或聚合时状态空间模型的固定区间估计AI 解读

Ansley & Kohn's (1982) geomertical derivation of the Kalman filter fixed-interval smoothing algorthm is extended to the case where some of the observations are missing or aggregated and the state covariance matrix may be singular. Estimates of missing observations are also obtained. The theory is then applied to the interpolation of missing data in mulativariate autoregressive-moving average modles.

时间序列模型中确定性趋势与季节成分的检验AI 解读

A univariate time series model can be set up as the sum of trend, seasonal and irregular components. The trend and seasonal components will normally be stochastic, but deterministic components arise as a special case. This paper develops a test that the trend and seasonal components are deterministic using the approach of Lehmann. The procedure is then extended to test for deterministic components in a model formulated in first differences. Both tests are exact and critical…

二项概率变化的似然比检验与累积和检验功效AI 解读

A simple iterative procedure is found for the exact null and alternative distributions of likelihood ratio, cumulative sum and related statistics for testing for a change in probability of a sequence of independent binomial random variables. It is concluded that the likelihood ratio test is slightly less powerful than the cumulative sum test near the middle of the sequence, but that the likelihood ratio test is much more powerful near the ends. An example from epidemiology…

平稳随机过程的 m 阶最大似然估计AI 解读

SUMMARY Sometimes the likelihood cannot be computed even introducing approximations whose effect is asymptotically negligible. To overcome this problem for inference from a stochastic process, we approximate the log likelihood by a sum whose generic term is the density function of the corresponding sample element conditional on the m most recent observations, for some m ≥ 0. General results on the asymptotic properties of the associated estimates are given, and numerical…