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论高效概率预测系统AI 解读

We study the asymptotic behaviour of probability forecasting systems, and discuss their usefulness as inferential tools for statistical problems such as model verification and selection. Our theoretical setting is the prequential, or predictive sequential, framework proposed by Dawid (1984). We study especially the notion of prequential efficiency of a forecasting system and present some new results. We focus on plug-in, or estimative, forecasting systems, where the forecast…

自回归时间序列模型的先验与成分结构AI 解读

Summary New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful…

利用金融与经济变量预测股票收益的非线性方法AI 解读

Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is capable of performing flexible nonlinear functional approximation. The nonlinear neural-network model is found to have better in-sample fit and out-of-sample forecasts compared to its…

稳定季节模式模型的预测：夏威夷旅游数据应用AI 解读

We propose several variations of the stable seasonal pattern (SSP) model first introduced by Marshall and Oliver and study their prediction procedures. Depending on the type of data (count data or continuous variable), we propose different treatments. Previously SSP models have been applied to trendless data or adapted to trending data in ad hoc ways. In the models considered here, conditional independence allows the seasonal pattern and trend to be modeled separately,…

时间序列分析中的子抽样与模型选择AI 解读

In this paper the subsampling method of Carlstein (1986) is used to estimate the risk of prediction for time series data. We extend Carlstein's result by proving strong consistency of the subsampling estimator and we propose a procedure for selecting a time series model empirically from a set of possibly nonnested and misspecified models by using estimated risk of prediction as a selection criterion. When this procedure is applied to the selection of the order of an…

自适应模型选择的协方差膨胀准则AI 解读

Summary We propose a new criterion for model selection in prediction problems. The covariance inflation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the data set. This criterion can be applied to general prediction problems (e.g. regression or classification) and to general prediction rules (e.g. stepwise regression, tree-based models and neural nets). As a…

汇率与货币基本面：长期限回归能告诉我们什么？AI 解读

The use of a new bootstrap method for small-sample inference in long-horizon regressions is illustrated by analysing the long-horizon predictability of four major exchange rates, and the findings are reconciled with those of an earlier study by Mark (1995). While there is some evidence of exchange rate predictability, contrary to earlier studies, no evidence is found of higher predictability at longer horizons. Additional evidence is presented that the linear VEC model…

汇率目标区模型：贝叶斯评估AI 解读

This paper develops a Bayesian approach to estimating exchange rate target zone models and rational expectations models in general. It also introduces a simultaneous-equation target zone model that incorporates stochastic realignment risk. Using FF/DM and IL/DM exchange rate data, we find that the signing of the 1987 Basle-Nyborg Agreement reduces both the magnitude and the likelihood of a central parity realignment, while the lagged exchange rate deviation from its central…

美国商业周期状态的预测AI 解读

This article examines the use of logistic classification methods for the identification and prediction of postwar U.S. business-cycle expansion and contraction regimes as defined by the National Bureau of Economic Research (NBER) reference turning-point dates. We present a coherent theoretical framework for this task using measures of discriminatory information. The analysis encompasses model selection, parameter estimation, and classification decision rules. We examine the…

大型回归问题中的快速离群诊断程序AI 解读

We propose a procedure for computing a fast approximation to regression estimates based on the minimization of a robust scale. The procedure can be applied with a large number of independent variables where the usual algorithms require an unfeasible or extremely costly computer time. Also, it can be incorporated in any high-breakdown estimation method and may improve it with just little additional computer time. The procedure minimizes the robust scale over a set of…

通过统计方法改进质量AI 解读

Process description, 62 6.2.2 Model formulation and fitting, 63 6.3 EPC Component: Developing the Control Rule, 63 6.3.1 The Minimum Mean Square Error Controller (MMSEC),64 6.3.2The Two-Step Ahead Forecasting Controller (TSAFC),64 6.3.3Clarke's Constrained Controller (CCC(r)), 65 6.4 Comparison of Control Strategies, 66 6.5 Robustness Properties of the Controllers, 67 6.6 EPC Component: Performance Under Assignable Causes, 69 6.7 SPC Component: Developing Monitoring…

大型回归问题中异常值诊断的快速程序AI 解读

We propose a procedure for computing a fast approximation to regression estimates based on the minimization of a robust scale. The procedure can be applied with a large number of independent variables where the usual algorithms require an unfeasible or extremely costly computer time. Also, it can be incorporated in any high-breakdown estimation method and may improve it with just little additional computer time. The procedure minimizes the robust scale over a set of…

通胀预测中的信息差异化解读AI 解读

We test the hypothesis associated with a standard assumption in the theoretical literature on learning: that economic agents interpret information identically. We use a data set based on a survey of Israeli business executives forecasting future inflation. One of the main advantages of using this data is that a major change in the inflation regime in 1985 can be treated as a natural experiment in new beliefs formation. We develop a methodology for testing this hypothesis and…

样本量与预测期同步增长时近单位根 AR(1) 模型的预测误差AI 解读

We develop asymptotic approximations to the distribution of forecast errors from an estimated AR(1) model with no drift when the true process is nearly I (1) and both the forecast horizon and the sample size are allowed to increase at the same rate. We find that the forecast errors are the sums of two components that are asymptotically independent. The first is asymptotically normal whereas the second is asymptotically nonnormal. This throws doubt on the suitability of a…

经验 SETAR 模型预测表现的蒙特卡洛研究AI 解读

In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the 'non-linearity' characterizes the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative…

摘要暂缺

含随机趋势成分回归模型中最大似然估计量的渐近性质AI 解读

This paper investigates the asymptotic properties of the maximum marginal likelihood estimator for a regression model with a stochastic trend component when the signal-to-noise ratio is near zero. In particular, the local level model in Harvey (1989, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press) and its variants where a time trend or an intercept is included are considered. A local-to-zero parameterization is…

基于共轭潜在过程的指数族状态空间模型AI 解读

Summary This paper introduces non-linear and non-Gaussian state space models with analytic updating recursions for filtering and prediction. This new class of models involves some well-known results in the theory of exponential models and of exponential dispersion models and the latent process is defined in such a way that both the filtering and the prediction distributions turn out to be conjugate to the observation distribution at each step. The corresponding analytic and…

通过核回归理解指数平滑AI 解读

Summary Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We…

数值方法下的贝叶斯经济学：利用先验信息开展计量分析与决策AI 解读

Theory and Basics.- A Quick Course in Bayesian Statistics and Decision Theory.- New Advances in Numerical Bayesian Techniques.- Applications in Econometrics.- Imposing Economic Theory.- Studying Parameters of Interest.- Unit Root and Cointegration Tests.- Model Specification Uncertainty.- Forecasting.- More Realistic Models Through Numerical Methods.- Applications to Economic Decision Making.- Decision Theory Applications.

论有效的点预测系统AI 解读

Summary Assume that a forecaster observes a sequence of random variables and issues predictions according to a point prediction systems, i.e. a rule which, at every time t, issues a point prediction for the next observation at time t+1. We introduce the concept of efficiency of a point prediction system, for the case that the joint distribution of the sequence of observations is known to belong to a parametric family, and performance is assessed by the long run sum of…

具有对称稳定冲击的最优单变量通胀预测AI 解读

Monthly inflation in the United States indicates non-normality in the form of either occasional big shocks or marked changes in the level of the series. We develop a univariate state space model with symmetric stable shocks for this series. The non-Gaussian model is estimated by the Sorenson-Alspach filtering algorithm. Even after removing conditional heteroscedasticity, normality is rejected in favour of a stable distribution with exponent 1�83. Our model can be used for…

协整与长期预测AI 解读

The authors consider the forecasting of cointegrated variables and they show that, at long horizons, nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. The authors' results highlight a potentially important deficiency of standard forecast accuracy measures--they fail to value the maintenance of cointegrating relationships among…

澳大利亚通胀建模AI 解读

This article develops an empirically constant, data-coherent, error-correction model for inflation in Australia. The level of consumer prices is a markup over domestic and import costs, with adjustments for dynamics and relative aggregate demand. The authors address issues of cointegration, general to specific modeling, dynamic specification, model evaluation and testing, parameter constancy, forecasting, and exogeneity. They also test this model against existing models of…