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贝叶斯预测模型的监控与适应AI 解读

Practical aspects of a new technique for monitoring and controlling the predictive performance of Bayesian forecasting models are discussed. The basic features of the approach to model monitoring introduced in a general setting in West (1986) are described and extended to a wide class of dynamic, nonnormal, and nonlinear Bayesian forecasting models. An associated method of automatically detecting and rejecting outliers and adapting models to abrupt structural changes in the…

系统性缺失观测下向量 ARMA 过程的预测AI 解读

The following two predictors are compared for time series with systematically missing observations: (a) A time series model is fitted to the full series Xt , and forecasts are based on this model, (b) A time series model is fitted to the series with systematically missing observations Y τ, and forecasts are based on the resulting model. If the data generation processes are known vector autoregressive moving average (ARMA) processes, the first predictor is at least as…

认股权证价格对股票价格变化的调整速度AI 解读

This article reports on work designed to measure the time required for a change in a stock's price to be fully reflected in the price of a warrant on that stock. The method employed to measure the adjustment speed is the bivariate transfer function technique of Box and Jenkins. An interesting aspect of the study is the use of trade-by-trade data for measuring stock and warrant returns. The evidence presented here suggests that warrant prices adjust quickly to changes in…

管理决策的定量方法AI 解读

ciation of the exchange rate. Then Lopez-Claros argues that this was not practical, since interest rate data were inaccurate for Latin American countries, in part because of the numerous government regulations. The final modeling paper is written by Adcock. He develops a model to forecast the percentage of network terminating points on a given network service as a function of time and data transmission speed. Also described is a model for the take-up of a new service.

使用计量模型预测：National Institute 模型的若干结果AI 解读

Abstract It is widely believed that the large econometric models cannot be used for forecasting without considerable intervention on the part of the forecaster. In this paper we challenge this view by reproducing a number of recent forecasts published by the National Institute but without the ad hoc interventions used at the time. We show that in no case would the forecast, produced by the model used mechanically, have been radically different from that actually published.…

汇率货币模型失效原因的实证考察AI 解读

Abstract The purpose of this paper is to attempt to provide measures of the relative importance of the principal causes of the failure of the monetary model of the exchange rate. Secondly, the random walk hypothesis for the exchange rate is tested. The methodology employed is new and has wide application elsewhere. It involves explicitly modelling the misspecification by time series techniques. The results confirm the importance of the breakdown of the PPP assumption but…

摘要暂缺

有助于实施 Litterman 时间序列插值程序的两项结果AI 解读

Litterman (1983) recently suggested a modification to the rameter to be accurate. Litterman's notation is followed Chow and Lin (1971) and Fernandez (1981) procedures for throughout. interpolating monthly data from an observed quarterly time series. ~riefl~ stated, the Litterman proceduri involves obtaining generalized least squares (GLS) forecasts of the monthly 2. THE LITTERMAN PROCEDURE IN BRIEF values of interest-the GLS estimates determined from quar- Letting Y* denote…

使用贝叶斯向量自回归进行预测：五年经验AI 解读

The results obtained in five years of forecasting with Bayesian vector autoregressions (BVAR's) demonstrate that this inexpensive, reproducible statistical technique is as accurate, on average, as those used by the best known commercial forecasting services. This article considers the problem of economic forecasting, the justification for the Bayesian approach, its implementation, and the performance of one small BVAR model over the past five years.

不同技术的预测准确性：美国宏观经济预测比较AI 解读

(1986). Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts. Journal of Business & Economic Statistics: Vol. 4, No. 1, pp. 5-15.

定义型变量的单变量 ARIMA 预测AI 解读

Many important variables in business and economics are neither measured nor measurable but are simply defined in terms of other measured variables. For instance, the real interest rate is defined as the difference between the nominal interest rate and the inflation rate. There are two ways to forecast a defined variable: one can directly forecast the variable itself, or one can derive the forecast of the defined variable indirectly from the forecasts of the constituent…

经济预测的组合AI 解读

A method for combining forecasts may or may not account for dependence and differing precision among forecasts. In this article we test a variety of such methods in the context of combining forecasts of GNP from four major econometric models. The methods include one in which forecasting errors are jointly normally distributed and several variants of this model as well as some simpler procedures and a Bayesian approach with a prior distribution based on exchangeability of…

摘要暂缺

不同技术的预测准确性：美国宏观经济预测比较——Spivey 评论AI 解读

W. Allen Spivey, [Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts]: Comment, Journal of Business & Economic Statistics, Vol. 4, No. 1 (Jan., 1986), pp. 19-22

不同技术的预测准确性：美国宏观经济预测比较——Litterman 评论AI 解读

Robert B. Litterman, [Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts]: Comment, Journal of Business & Economic Statistics, Vol. 4, No. 1 (Jan., 1986), pp. 17-19

不同技术的预测准确性：美国宏观经济预测比较——Granger 评论AI 解读

C. W. J. Granger, [Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts]: Comment, Journal of Business & Economic Statistics, Vol. 4, No. 1 (Jan., 1986), pp. 16-17

使用贝叶斯向量自回归进行预测：五年经验AI 解读

The results obtained in five years of forecasting with Bayesian vector autoregressions (BVAR's) demonstrate that this inexpensive, reproducible statistical technique is as accurate, on average, as those used by the best known commercial forecasting services. This article considers the problem of economic forecasting, the justification for the Bayesian approach, its implementation, and the performance of one small BVAR model over the past five years.

向量时间序列技术在宏观经济预测中的应用AI 解读

A wide variety of time series techniques are now used for generating forecasts of economic variables, with each technique attempting to summarize and exploit whatever regularities exist in a given data set. It appears that many researchers arbitrarily choose one of these techniques. The purpose of this article is to provide an example for which the choice of time series technique appears important; merely choosing arbitrarily among available techniques may lead to suboptimal…

经济预测的统计方法AI 解读

A recently developed statistical model, called Bayesian vector autoregression, has proven to be a useful tool for economic forecasting. Such a model today forecasts a strong resurgence of growth in the second half of 1985 and in 1986.

参数经估计的状态空间模型预测均方误差AI 解读

We obtain a conditional prediction mean squared error for a state space model with estimated parameters. An important application of our results is the derivation of conditional forecast and interpolation mean squared errors for autoregressive-moving average models with estimated parameters. We also obtain the conditional mean squared error for filtered and smoothed estimates of the state vector.

基于校准的经验概率AI 解读

Probability forecasts for a sequence of uncertain events may be compared with the outcomes of those events by means of a natural criterion of empirical validity, calibration. It is shown that any two sequences of forecasts which both meet this criterion must be in asymptotic agreement. These agreed values can then be considered as correct objective probability forecasts for the particular sequence of outcome results obtained. However, the objective forecasts vary with the…

回归的 Bootstrap 预测区间AI 解读

Bootstrap prediction intervals provide a nonparametric measure of the probable error of forecasts from a standard linear regression model. These intervals approximate the nominal probability content in small samples without requiring specific assumptions about the sampling distribution. Empirical measures of the prediction error rate motivate the choice of these intervals, which are calculated by an application of the bootstrap. The intervals are contrasted to other…

误设自回归时间序列模型中预测量的性质AI 解读

This article investigates major effects of misspecification in stationary linear time series models when we fit a pth-order autoregressive model. The true model can be an autoregressive moving average model. We derive the formulas of bias and mean squared error (MSE) of the least squares estimator and the hth period ahead prediction MSE in the time domain. Contrary to previous studies, the process in estimation is not necessarily independent of the process in prediction, and…

时间序列回归模型的高效估计与预测AI 解读

For a time series regression model with correlated residuals we show how to compute efficiently the likelihood, predict future values and interpolate missing values. As an illustration we consider a regression model with autoregressive moving average residuals.