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预测组合的混合模型方法AI 解读

A multiprocess mixture-model approach to combining forecasts from alternative sources is proposed. This approach extends the Granger-Ramanathan method by allowing the weights used in producing the combination forecast to vary over time. In addition, the procedure discounts outlying data points that arise during time periods when all of the competing forecasts miss the mark. An empirical comparison with traditional and more recently proposed.combination methods demonstrates…

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用自助法辅助选择向量时间序列的近似表示AI 解读

In this article, a procedure is presented to use the bootstrap in choosing the best approximation in terms of forecasting performance for the equivalent state-space representation of a vector autoregressive model. It is found that the proposed procedure, which uses each approximator's forecasting performance, can enhance considerably an approach based simply on the estimated Hankel singular values.

预测分歧与预测不确定性的关系：调查数据与 ARCH 模型证据AI 解读

Abstract This paper examines empirically the relationship between measures of forecast dispersion and forecast uncertainty from data on inflation expectations from the Livingston survey series and the Survey Research Center (SRC) survey series. Because the survey series do not provide probabilistic forecasts of inflation, we derive measures of inflation uncertainty by modelling the conditional variance of the inflation forecast errors from the survey series as an…

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季节性时间序列建模与预测的另一种方法AI 解读

This article proposes an alternative methodology for modeling and forecasting seasonal series. The approach is in the Bayesian autoregression tradition pioneered by Doan, Litterman, and Sims and builds seasonality directly into the prior of the coefficients of the model by means of a set of uncertain linear restrictions. As an illustration, the method is applied to 10 U.S. quarterly macroeconomic series. For each series, the author compares the forecasting performance of a…

混沌系统的似然与贝叶斯预测AI 解读

There has recently been considerable interest in both applied disciplines and in mathematics, as well as in the popular science literature, in the areas of nonlinear dynamical systems and chaotic processes. By a nonlinear, deterministic dynamical system, we mean a time series in which, starting at some initial condition, the values of the series are some fixed, nonlinear function of the previous states. One of the more intriguing aspects of these models is their propensity…

潜在真实时间序列及其指标：一种替代方法AI 解读

de Leeuw and McKelvey proposed a method for using two imperfect indicators to predict the values of an unobserved series. In this article we suggest an alternative method that uses the multiple indicator approach to model the true time series and its indicators. We examine the identification status of the model, estimate the parameters by maximum likelihood, and use the Kalman filter to obtain predictions of the unobserved series. The method is applied to the U.S. gross…

混沌系统的似然与贝叶斯预测AI 解读

There has recently been considerable interest in both applied disciplines and in mathematics, as well as in the popular science literature, in the areas of nonlinear dynamical systems and chaotic processes. By a nonlinear, deterministic dynamical system, we mean a time series in which, starting at some initial condition, the values of the series are some fixed, nonlinear function of the previous states. One of the more intriguing aspects of these models is their propensity…

AR(1) 模型中预测误差的联合分布AI 解读

Second-order asymptotic expansion approximations to the joint distributions of dynamic forecast errors and of static forecast errors in the stationary Gaussian pure AR(1) model are derived. The approximation to the dynamic forecast errors distribution can be expressed as a multivariate normal distribution with modified mean vector and covariance matrix, thus generalizing the results of Phillips [12]. However, the approximation to the static forecast errors distribution…

通过向量自回归模型估计正交化脉冲响应AI 解读

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.

The authors propose some Stein-rule combination forecasting methods that are designed to ameliorate the estimation of risk inherent in making operational the variance-covariance method for constructing combination weights. By Monte Carlo simulation, it is shown that this amelioration can be substantial in many cases. Moreover, generalized Stein-rule combinations are proposed that offer the user the opportunity to enhance combination forecasting performance when shrinking the…

暂定死亡率序列的调整：含结构化测量误差的动态线性模型AI 解读

The authors "consider the problem of adjusting provisional time series using a bivariate structural model with correlated measurement errors. Maximum likelihood estimators and a minimum mean squared error adjustment procedure are derived for a provisional and final series containing common trend and seasonal components. The model also includes measurement errors common to both series and errors that are specific to the provisional series. [The authors] illustrate the…

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时间序列重抽样：估计养老金抗通胀方案下的损失概率AI 解读

Abstract Any pension protection formula that falls short of complete compensation for inflation has associated with it a time‐series of probability distributions of future purchasing power losses. A method of estimating those distributions is proposed and applied. The method is based on the idea of representing inflation as a multivariate time‐series process and using a model fitted to historical data to generate a large artificial sample of ‘realized’ inflation sequences by…

弥散卡尔曼滤波AI 解读

The Kalman recursion for state space models is extended to allow for likelihood evaluation and minimum mean square estimation given states with an arbitrarily large covariance matrix. The extension is computationally minor. Application is made to likelihood evaluation, state estimation, prediction and smoothing.

一阶自回归预测的偏差AI 解读

The exact finite sample behavior is investigated on the bias of multiperiod leastsquares forecasts in the normal autoregressive model y t = α + β y t –1 + u t . Necessary and sufficient conditions are given for the existence of the bias and an expression is presented which we use to obtain exact numerical results for finite samples. The unit root and near unit root behavior is studied in detail and some popular preconceptions about the behavior of the bias are shown to be…

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能否改进非农就业增长的初步估计？AI 解读

We explore the feasibility of improving upon the preliminary estimates of payroll employment growth from the U.S. Bureau of Labor Statistics by predicting subsequent revisions to these estimates, using the preliminary estimates themselves and other information available concurrently. Results of statistical tests suggest that the preliminary estimates can be improved upon; that is, they are not "efficient forecasts" of the revised estimates. The improvement of preliminary…

日元兑美元外汇市场风险溢价的时间序列性质AI 解读

Abstract In this paper a VAR model is employed to construct a measure of the conditional expectations of the future yen/dollar spot rate. This measure allows us to examine the dynamics of an ex‐ante time‐series for the risk premium in the market. The VAR model produces ‘better’ forecasts than the survey responses for turbulent periods such as 1981–1982 and 1984–1985. The VAR‐generated expectations are then used to construct a risk premium time‐series. This risk premium…

参数不确定性对单位根与趋势平稳模型预测方差和置信区间的影响AI 解读

Abstract In this paper I describe the effect of parameter uncertainty on the way conditional forecast variances grow as the forecast horizon increases. Without parameter uncertainty, forecast variances for the unit root model grow linearly with the forecast horizon while with the trend stationary model they are bounded. With parameter uncertainty, however, I find that for both the unit root and the trend stationary models, forecast variances grow with the square of the…

The results of forecasting experiments based on an error correction mechanism (ECM) model and various types of vector autoregressive (VAR) and Bayesian vector autoregressive (BVAR) models are presented. A Bayesian error correction mechanism (BECM) model is also tested. This model represents a hybrid of the BVAR and ECM models. The results from experiments using fifty industries and monthly Ohio labor market data demonstrate that the ECM model produces forecasts with much…