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用结构模型与 Box–Jenkins 模型预测经济时间序列：案例研究AI 解读

The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series and the forecasting…

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《用结构模型与 Box–Jenkins 模型预测经济时间序列：案例研究》答复AI 解读

A. C. Harvey, P. H. J. Todd, [Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study]: Response, Journal of Business & Economic Statistics, Vol. 1, No. 4 (Oct., 1983), pp. 313-315

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时间序列过去与未来的典型相关：定义与理论AI 解读

The concepts of canonical correlations and canonical components are familiar ideas in multivariate statistics. In this paper we extend these notions to stationary time series with a view to determining the most predictable aspect of the future of a time series. We relate properties of the canonical description of a time series to well known structural properties of the series such as (i) rational spectra (i.e., ARMA series), (ii) strong mixing, (iii) absolute regularity, etc.

动态非线性理性预期模型的求解与极大似然估计AI 解读

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper.The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates.When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models.It is, however, more flexible and general…

含趋势与季节性的时间序列预测AI 解读

A maximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelve-step-ahead prediction models can be different. The different models exhibit the relative optimality properties for which they were designed. The results are related to open…

自回归误差回归模型预测的小样本性质AI 解读

Monte Carlo methods are used to examine the small-sample properties of various estimators of asymptotic prediction variance (APV) from the regression model with autoregressive errors. Two practical estimators of APV, one of which includes terms reflecting parameter estimation and one of which excludes these terms, are compared to the mean squared error of prediction for regression models with different autocorrelation of the errors, different sample sizes, and different…

自回归误差回归模型预测的小样本性质AI 解读

Monte Carlo methods are used to examine the small-sample properties of various estimators of asymptotic prediction variance (APV) from the regression model with autoregressive errors. Two practical estimators of APV, one of which includes terms reflecting parameter estimation and one of which excludes these terms, are compared to the mean squared error of prediction for regression models with different autocorrelation of the errors, different sample sizes, and different…

从实际选择中恢复风险偏好AI 解读

SINCE ITS INTRODUCTION, the expected utility hypothesis has been widely used in the construction of economic models. More recently, attention has focused on the conditions under which it is possible in principle to recover individual investors' risk preferences from their demand for assets (Dybvig and Polemarchakis [2]). This paper represents a first attempt to recover preferences operationally from data on the actual demand for assets. Numerous difficulties are encountered…

多元自回归指数模型的若干结果AI 解读

We discuss methods for modelling multivariate autoregressive time series in terms of a smaller number of index series which are chosen to provide as complete a summary as possible of the past information contained in the original series necessary for prediction purposes. The maximum likelihood method of estimation and asymptotic properties of estimators of the coefficients which determine the index variables, a well as the corresponding autoregressive coefficients, are…

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…

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…

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…

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…

多元自回归指数模型的若干结果AI 解读

We discuss methods for modelling multivariate autoregressive time series in terms of a smaller number of index series which are chosen to provide as complete a summary as possible of the past information contained in the original series necessary for prediction purposes. The maximum likelihood method of estimation and asymptotic properties of estimators of the coefficients which determine the index variables, a well as the corresponding autoregressive coefficients, are…

非对称滤波器对季节因子修订的影响AI 解读

Recent data seasonally adjusted by moving average methods are subject to revisions due to differences in the properties of the linear filters for the same seasonal adjustment when later data become available. This article introduces a measure of the total revision associated with the concurrent and forecasting seasonal filters and applies it to the Census Method II-X-11 variant and the X-11-ARIMA method. To consider the fact that the spectrum of many economic indicators…

非对称滤波器对季节因子修订的影响AI 解读

Recent data seasonally adjusted by moving average methods are subject to revisions due to differences in the properties of the linear filters for the same seasonal adjustment when later data become available. This article introduces a measure of the total revision associated with the concurrent and forecasting seasonal filters and applies it to the Census Method II-X-11 variant and the X-11-ARIMA method. To consider the fact that the spectrum of many economic indicators…

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自回归条件异方差与英国通胀方差估计AI 解读

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period…

时间序列中三角函数成分的估计AI 解读

Estimation in time series with an unknown number of deterministic trigonometric components with unknown amplitudes and frequencies is considered. A stepwise procedure is used. At each step the frequency of the largest term in the periodogram of the residual series is used as a starting value for finding the best frequency in the least squares sense. The procedure is stopped when there are no further significant harmonic components, when tested by a multiple-test procedure.…

Data Resources, Inc., Regional Load-Curve Models, vols. 1 and 3 (Aug., Sept. 1981). Engle, Robert, Clive W. J. Granger, Allen Mitchem, and Ramu Ramanathan, Some Problems in the Estimation of Daily Load Shapes and Peaks, Modelling and Analysis ofElectricity Demand by Time-of-Day, Electric Power Research Institute (EPRI), EA-1304, Dec. 1979. Engle, Robert F., Clive W. J. Granger, and Ramu Ramanathan, Regional Load Curs e Models: QUERJ's Model Long Run Forecasts and…