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最小预测均方误差的估计AI 解读

Bloomfield (1973) and Jones (1964) have discussed the estimation of the error of prediction of a time series. Their results use the asymptotic normality of their estimates and we attempt to examine the validity of this approximation in the simplest case. We show that one can approximate the distribution of the mean square error in small-sample situations, and in consequence obtain some idea of the rate of convergence to normality.

最小预测均方误差的估计AI 解读

Bloomfield (1973) and Jones (1964) have discussed the estimation of the error of prediction of a time series. Their results use the asymptotic normality of their estimates and we attempt to examine the validity of this approximation in the simplest case. We show that one can approximate the distribution of the mean square error in small-sample situations, and in consequence obtain some idea of the rate of convergence to normality.

McGraw-Hill 预期调查数据的预测准确性AI 解读

This study analyzes the forecast accuracy of the McGraw-Hill (M-H) anticipations with one- to four-year horizons of investment, sales and capacity for major manufacturing industries for the period 1948–71. Average absolute percentage errors are presented for each anticipation series. In comparison to the BEA survey of one-year investment anticipations, the M-H data are generally less accurate. On the other hand, in forecasting experiments the M-H series are shown to be more…

McGraw-Hill 预期调查数据的预测准确性AI 解读

This study analyzes the forecast accuracy of the McGraw-Hill (M-H) anticipations with one- to four-year horizons of investment, sales and capacity for major manufacturing industries for the period 1948–71. Average absolute percentage errors are presented for each anticipation series. In comparison to the BEA survey of one-year investment anticipations, the M-H data are generally less accurate. On the other hand, in forecasting experiments the M-H series are shown to be more…

混合自回归移动平均过程的精确似然函数AI 解读

Box & Jenkins (1970) develop a strategy for the analysis and forecasting of time series, based on the construction, after suitable differencing, of autoregressive-moving average models. They present a model building procedure which works very well for most general purposes, and in addition derive the exact likelihood function for both pure autoregressive and pure moving average processes, in the latter case this being a new result. In this note it is shown that a similar…

Wiener–Kolmogorov 预测器的渐近性质（一）AI 解读

Summary “Windowed” estimates of the spectral density function are used to carry out the factorization of the sample spectral density function and hence estimate the Wiener-Kolmogorov prediction coefficients. Expressions are derived for the asymptotic mean and variance of all the statistics necessary to estimate the predictor. The covariances and asymptotic normality of these estimates are also investigated. In particular, an expression for the asymptotic mean-square error or…

人口转变后出生数预测：具有序列相关生育率的随机更新模型AI 解读

Demographic forecasting techniques fail with post-transition populations dominated by fluctuating fertility; time series analysis of fertility can improve the forecasts. This article develops the optimal forecast and its variance for births to an age-structured population subject to serially correlated random fertility. The white noise, first-order autoregressive, second-order autoregressive and random walk fertility specifications are analyzed, each leading to different…

摘要暂缺

随机参数估计的递归解AI 解读

Some applications of the Wiener-Kolmogorov and Bayes approaches are discussed with regard to the construction of recursive equations for the best linear estimator of a stochastic parameter. Solutions are given for the prediction, filtering and smoothing cases when the parameter follows a vector Markov process and is observed with error. The Bayes solution is used to generate a canonical factorization of the autocovariance generating function of the observed series.…

计量经济模型动态模拟中预测的渐近分布AI 解读

This paper considers the asymptotic distribution of forecasts made several time periods in the future. Such forecasts typically arise in the dynamic simulation of an econometric model.

THE CLASSIC PAPER by Grunfeld and Griliches [3] contains many instructive insights into question of circumstances under which an aggregate dependent variable may be forecasted more precisely with a model based on aggregate variables as opposed to an aggregate of forecasts from individual equations. The statistical model they employ is standard regression framework, a single at macro level and a set of seemingly unrelated regressions (to use Zellner's term) at level. Under…

混合自回归滑动平均过程的精确似然函数AI 解读

Box & Jenkins (1970) develop a strategy for the analysis and forecasting of time series, based on the construction, after suitable differencing, of autoregressive-moving average models. They present a model building procedure which works very well for most general purposes, and in addition derive the exact likelihood function for both pure autoregressive and pure moving average processes, in the latter case this being a new result. In this note it is shown that a similar…

联邦储备委员会敏感价格指数预测能力的评价AI 解读

This article reviews the historical performance of the Federal Reserve Board Sensitive Price Index (SPI) in accord with the standards established for such an index by the Price Statistics Review Committee. Several empirical tests are outlined which can be used to assess the predictive performance of any leading indicator. These tests indicate that the SPI performs well as an indicator for business conditions but has little to offer for the problem of predicting price level…

摘要暂缺

美联储敏感价格指数预测能力的评价AI 解读

This article reviews the historical performance of the Federal Reserve Board Sensitive Price Index (SPI) in accord with the standards established for such an index by the Price Statistics Review Committee. Several empirical tests are outlined which can be used to assess the predictive performance of any leading indicator. These tests indicate that the SPI performs well as an indicator for business conditions but has little to offer for the problem of predicting price level…

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…

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…

摘要暂缺

基于贝叶斯方法的随机差分方程多期预测AI 解读

Given n observations on a system of linear stochastic difference equations with appropriate initial conditions, and given a prior density (possibly diffuse) of its parameters, this paper obtains the predictor of the time series k periods into the future with minimum mean squared error. Completely analytical solution is given for predictions from the first-order univariate system, and, in the general higher-order multivariate case, for k up to 5. econometric equations to…

标量时间序列频谱的指数模型AI 解读

SUMMARY Existing procedures for obtaining linear prediction formulae for time series may be placed in two categories. The first consists of methods which involve fitting parametric models of the autoregressive or autoregressive moving average type. The second involves the factorization of an estimated spectral density function; this is essentially a nonparametric procedure. The procedure suggested in this paper contains elements of both of the above. Formally, it involves…

时间序列预测的误差AI 解读

Parametric and nonparametric procedures for the prediction of a time series are discussed. In each case the increase in the mean squared error of prediction, over its minimum level, due to the use of estimated spectra is assessed. The fitting of simple parametric models as approximations is also discussed.

美国石油炼制业的经济—线性规划模型AI 解读

The linking of statistically estimated relationships with an engineering linear programming model offers important potentials for simulation and forecasting models of industries. This article describes such a model of the U.S. petroleum refining industry. Econometric techniques are used to specify product demands, prices, and some technical adjustments. A linear programming specification of the production function, assuming cost minimization, determines crude oil input…

聚合对自回归模型预测的影响AI 解读

The article shows that if the original variable follows a pth order autoregressive system then the non-overlapping moving sum follows a pth order autoregression with at most a pth order moving-average of an independent sequence regardless of the length of the summation. From such an aggregate model we derive the optimal predictor of the aggregate variable and show that it performs remarkably well compared to the optimal disaggregate predictor. The article contains both…

美国石油炼制行业的经济—线性规划模型AI 解读

The linking of statistically estimated relationships with an engineering linear programming model offers important potentials for simulation and forecasting models of industries. This article describes such a model of the U.S. petroleum refining industry. Econometric techniques are used to specify product demands, prices, and some technical adjustments. A linear programming specification of the production function, assuming cost minimization, determines crude oil input…