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动态模型的最优递归估计AI 解读

This article checks, using both real and simulated data, the effectiveness of modern adaptive techniques to track the parameters of time-varying dynamic models. The real case studies concern a bone marrow transplant data set published by Tong, the gas furnace model of Box and Jenkins, and two series of West German interest rates. Simulation studies focus on ARX models with smoothly and suddenly changing parameters. The general approach is to compare the fitting-forecasting…

动态模型的最优递归估计AI 解读

This article checks, using both real and simulated data, the effectiveness of modern adaptive techniques to track the parameters of time-varying dynamic models. The real case studies concern a bone marrow transplant data set published by Tong, the gas furnace model of Box and Jenkins, and two series of West German interest rates. Simulation studies focus on ARX models with smoothly and suddenly changing parameters. The general approach is to compare the fitting-forecasting…

在未知平稳化方式下建模股票价格AI 解读

Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and…

宏观经济学家应了解的单位根问题：贝叶斯视角AI 解读

This paper summarizes recent Bayesian research on unit roots for the applied macroeconomist in the way Campbell and Perron [8] summarized the classical unit roots perspective. The appropriate choice of a prior is discussed. In recognizing a consensus distaste for explosive roots, I find the popular Normal-Wishart priors centered at the unit root to be reasonable provided they are modified by concentrating the prior mass for the time trend coefficient toward zero as the…

经济时间序列的贝叶斯预测AI 解读

A model is suggested to forecast economic time series. This model incorporates some innovative ideas of Harrison and Stevens [20] for building into the forecasting process important external shocks to the systems. Thus the occurrence of possibly significant real-world events may cause a fundamental change in the time series in question. The Jeffreys-Savage (JS) Bayesian theory of hypothesis testing is used to test the hypothesis that a particular event has been such as to…

摘要暂缺

经济周期阶段及其转移动态AI 解读

This article examines differences in expansionary and contractionary phases of the business cycle. By extending the nonlinear Markov-switching estimation method of J. D. Hamilton to incorporate time-varying probabilities of transitions between the phases, the marginal benefits of the time-varying transition probability Markov-switching model are highlighted. Using this technique, the author documents the high correlation between the evolution of the phases inferred from the…

利用卡尔曼滤波估计、预测与插补非平稳序列AI 解读

We show how our definition of the likelihood of an autoregressive integrated moving average (ARIMA) model with missing observations, alternative to that of Kohn and Ansley and based on the usual assumptions made in estimation of and forecasting with ARIMA models, permits a direct and standard state-space representation of the nonstationary (original) data, so that the ordinary Kalman filter and fixed point smoother can be efficiently used for estimation, forecasting, and…

椭圆轮廓误差下的贝叶斯卡尔曼滤波AI 解读

The basic recursive equations of the Kalman filter, for independent normally distributed error sources in both the observation and the system equations, are shown to hold for a larger class of distributions. It is shown that, for error sources following jointly a general elliptical distribution, the conditional posterior distribution of the parameters and the one-step ahead predictive distribution at every stage t are also elliptical. This generalizes some recent results of…

非平稳状态空间模型的快速似然计算与预测AI 解读

A recursive procedure for initializing the Kalman filter is displayed. The recursion is for nonstationary state space models. The procedure imposes small computational and programming burden over and above the Kalman filter. The procedure is superior to other suggested approaches in both computational speed and general applicability. General properties of the method are investigated. Details of the initialization for the ARIMA (p,d,q) and basic structural models are…

用于预测的神经网络模型AI 解读

In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called onedimensional Newton method, an updating algorithm currently popular in the neural net…

用于预测的神经网络模型AI 解读

In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called onedimensional Newton method, an updating algorithm currently popular in the neural net…

变换模型预测能力的简单规格检验AI 解读

A specification test is proposed to test the adequacy of the implicit conditional mean specification in a transformation model. Regardless of the transformation under the null, the statistic is computable from ordinary least squares output via auxiliary ordinary least squares regressions. Further, the test presumes no particular form for the conditional variance of the transformed and untransformed regressands. A simple goodness-of-fit measure that can be used to compare…

工具变量估计下细分模型与加总模型的选择AI 解读

A choice criterion is proposed for discriminating between disaggregate and aggregate models estimated by the instrumental variables method. The criterion, based on prediction errors, represents a generalization of criteria developed in the context of classical regressions models. The paper also derives general tests for aggregation bias in the instrumental variables context. The criterion and tests are applied in an analysis of U.K. employment demand. It is shown that a…

结构时间序列模型的预测表现AI 解读

Although theoretical research on the properties of structural time-series models has regularly appeared in the literature, there is as yet scant evidence on the forecasting performance of structural models relative to more traditional methods. This study compares the empirical performance of structural time-series models to four methods that are similar in complexity using 111 business and economic time series. The structural approach appears to perform quite well on annual,…

椭圆轮廓误差下的贝叶斯卡尔曼滤波AI 解读

SUMMARY The basic recursive equations of the Kalman filter, for independent normally distributed error sources in both the observation and the system equations, are shown to hold for a larger class of distributions. It is shown that, for error sources following jointly a general elliptical distribution, the conditional posterior distribution of the parameters and the one-step ahead predictive distribution at every stage t are also elliptical. This generalizes some recent…

连续比例向量的时间序列模型AI 解读

SUMMARY A vector of continuous proportions consists of the proportions of some total accounted for by its constituent components. An example is the proportions of world motor vehicle production by Japan, the USA and all other countries. We consider the situation where time series data are available and where interest focuses on the proportions rather than the actual amounts. Reasons for analysing such times series include estimation of the underlying trend, estimation of the…

自回归时间序列均值与方差变化的贝叶斯推断及预测AI 解读

This article is concerned with statistical inference and prediction of mean and variance changes in an autoregressive time series. We first extend the analysis of random mean-shift models to random variance-shift models. We then consider a method for predicting when a shift is about to occur. This involves appending to the autoregressive model a probit model for the probability that a shift occurs given a chosen set of explanatory variables. The basic computational tool we…

多步预测中极大似然估计的稳健性：指数平滑情形AI 解读

We extend the argument initiated by Cox (1961) that the exponential smoothing formula can be made more robust for multi-step forecasts if the smoothing parameter is adjusted as a function of the forecast horizon l. The consistency property of the estimator which minimizes the sum of squares of the sample l-step ahead forecast errors makes the robustness result useful in practice. We also generalize the consistency result to some other parsimonious nonstationary models which…

摘要暂缺

基于马尔可夫转换模型的经济周期分析AI 解读

This article explores the Hamilton Markov-switching model through an analysis of the business cycles of eight developed market economies. Forecasting and specification tests suggest only marginal improvements over linear autoregressive models. Yet filtered and smoothed conditional probabilities indicate turning points in business cycles that closely correlate with turning points from traditional methods. Tests regarding the asymmetry of business cycles reject the null of…

作为不可观测成分的远期外汇溢价：札记AI 解读

We reconsider the signal-extraction approach to measuring premia in the pricing of forward foreign exchange, put forward by Wolff, in which the difference between the forward rate and the associated future spot rate is modeled as an autoregressive moving average (ARMA) model for the risk premium buried in a white-noise forecast error. We point out that an ARMA model for the risk premium is not always identifiable from information on the difference between the forward rate…

多元时间序列均值变化预测：企业失败预测应用AI 解读

A firm in the early stages of financial distress exhibits characteristics different from those of healthy firms. As the economic condition of a firm worsens, its financial characteristics shift toward those of failed firms. Practitioners in the financial sector have long been interested in the early detection of a firm's slide toward insolvency. Several models have been developed with this purpose in mind, but these older models are static in nature. Therefore, a need exists…

通过交叉验证选择线性模型AI 解读

We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the C p , and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of…