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条件异方差时间序列模型AI 解读

Under the traditional linear time series or regression setting, the conditional variance of one-step-ahead prediction is time invariant. Experience in conjunction with data analysis, however, suggests that the variability of a process might well depend on the available information. This reality has motivated extensive research to relax the constant variance assumption imposed by the traditional linear time series model, and several classes of generalized parametric models…

公布效应下的重复抽样AI 解读

The generation and public dissemination of information from surveys or polls may be expected to modify or condition the private attitudes or intentions it is hoped to measure. Thus the application of statistics to human affairs is often characterized by a reactivity or reflexivity that may even negate its findings or predictions. Bandwagon, funding, or floating voter effects arising from the publication of political polls are well known. Forecasts of expected visitor numbers…

存在调查结果发布效应时的重复抽样AI 解读

The generation and public dissemination of information from surveys or polls may be expected to modify or condition the private attitudes or intentions it is hoped to measure. Thus the application of statistics to human affairs is often characterized by a reactivity or reflexivity that may even negate its findings or predictions. Bandwagon, funding, or floating voter effects arising from the publication of political polls are well known. Forecasts of expected visitor numbers…

自回归模型中若干预测量的有限样本性质AI 解读

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors are derived up to O ( T −1 ), where T is the sample size. Examining the formulas of the asymptotic expansions, we find that except for the Yule-Walker type predictor, the other three…

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早期 GNP 估计的预测误差、测量误差与良好性质检验AI 解读

Revisions of the early GNP estimates may contain elements of measurement errors as well as forecast errors. These types of error behave differently but need to satisfy a common set of criteria for well-behavedness. This article tests these criteria for U.S. GNP revisions. The tests are similar to tests of rationality and are based on the generalized method of moments estimator. The flash, 15-day, and 45-day estimates are found to be ill behaved, but the 75-day estimate…

含共同因子的聚合所产生的含义AI 解读

Many observed macrovariables are simple aggregates over a large number of microunits. It is pointed out that the generating process of the macrovariables is largely determined by the common factors in the generating mechanisms of the microvariables, even though these factors may be very unimportant at the microlevel. It follows that macrorelationships are simpler than the complete microrelationships, but that empirical investigations of microrelationships may not catch those…

宏观计量模型比较与评价技术：实践评估AI 解读

Abstract This paper examines four model comparison techniques in the setting of an actual large‐scale U.S. macroeconomic model. The techniques include formal hypothesis tests, mean squared error analysis, and two new tests based on the complementary conditions that the structural and reduced form disturbances should be orthogonal to the predetermined variables. The latter two tests can be conveniently implemented by regressing either structural residuals or reduced form…

非平稳过程预测误差的渐近分布及相关拟合检验AI 解读

Limit distributions of prediction errors when the parameters are estimated are obtained for a general class of nonstationary processes that includes supercritical branching processes and explosive autoregressive processes as typical examples. The estimated prediction errors are used to construct new tests of fit whose limit distributions are also derived.

季节时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

季节性时间序列的趋势与增长率估计AI 解读

We consider the problem of trend estimation from an ARIMA-model-based perspective. Given that the time series of interest is well represented by a multiplicative seasonal ARIMA model (Box and Jenkins 1970), a method is presented for estimating the trend of this series as a component of the model's forecast function. This method is applied to the “airline model,” a commonly occurring model form for economic and social time series. For those numerous series that are rendered…

构建线性时间序列模型的候选集合AI 解读

The paper applies selection criteria to order determination in linear time series models in a less mechanical fashion than is often found. This is achieved via posterior odds ratios and by applying concepts of grades of evidence advanced by Jeffreys (1961). It is shown how alternative specifications may be compared and this leads to the development of a practical method for determining a portfolio of different models, each of which may be regarded as reasonable for the data…

使用条件变量或因果变量的预测后果AI 解读

Forecasts based on two information sets, one of which includes the other plus additional causal variables are considered. Given a general cost function of forecast errors, it is shown that the expected cost is smaller for the information set that includes the causal variables.

西班牙汽车事故指标分析：多重时间序列方法AI 解读

The main objective of this article is to specify and estimate a model for the car accident rates in Spain to improve input for the decision-making process for insurance companies and provide useful information for traffic authorities. The prediction performance of the model is also analyzed in an attempt to verify the improvement in prediction that takes place when more elaborate models are used.

时变参数与结构汇率模型的样本外预测表现AI 解读

Varying-parameter estimation techniques based on recursive application of the Kalman filter are used to improve the predictive performance of a class of monetary exchange rate models. I find that allowing estimated parameters to vary over time enhances the models' forecasting performance for the dollar–pound, dollar–mark, and dollar–yen exchange rates. Contrary to earlier results in the literature, ex-post forecasts for the dollar-mark rate compare favorably with those…

稳定季节模式下的贝叶斯预测AI 解读

A multiplicative seasonal forecasting model for cumulative events in which, conditional on end- of-season totals being given and seasonal shape being known, it is shown that events occurring within the season are multinomially distributed is presented. The model uses the information contained in the arrival of new events to obtain a posterior distribution for end-of-season totals. Bayesian forecasts are obtained recursively in two stages: first, by predicting the expected…

确定线性时间序列模型的候选集合AI 解读

The paper applies selection criteria to order determination in linear time series models in a less mechanical fashion than is often found. This is achieved via posterior odds ratios and by applying concepts of grades of evidence advanced by Jeffreys (1961). It is shown how alternative specifications may be compared and this leads to the development of a practical method for determining a portfolio of different models, each of which may be regarded as reasonable for the data…

利用时间序列模型联合预测美国婚内生育、婚姻、出生与结婚AI 解读

This article presents a new approach to forecasting U.S. marital fertility, nuptiality, births, and marriages. The analysis represents a wedding of demographic and statistical time series in models amenable to Box-Jenkins techniques of model identification, estimation, diagnosis, and forecasting. The models demonstrate the advantages in this approach in forecasting both rates and events as opposed to the common practice of simply forecasting events. Using the best models of…

初步数据误差及其对联立方程模型预测误差的影响AI 解读

Econometric models, especially when designed for forecasting purposes, tend to use updated economic series, the last figures(s) of which embed first-published or preliminary data errors. This article identifies and assesses the contribution of errors in preliminary data to the forecast error and to the forecast error variance of linear dynamic simultaneous equation models. The effect of preliminary data errors is shown to be pervasive, although not necessarily weighty. The…

抽样未来：单变量时间序列模型预测的贝叶斯方法AI 解读

The Box–Jenkins methodology for modeling and forecasting from univariate time series models has long been considered a standard to which other forecasting techniques have been compared. To a Bayesian statistician, however, the method lacks an important facet—a provision for modeling uncertainty about parameter estimates. We present a technique called sampling the future for including this feature in both the estimation and forecasting stages. Although it is relatively easy…

使用单边滤波器进行时间序列季节调整AI 解读

This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems…

利用预期产出调查数据的英国制造业就业计量模型AI 解读

Abstract A model of U.K. manufacturing employment is estimated in which output expectations data are derived from Confederation of British Industry (CBI) survey information. The output expectations terms are highly significant, and equations including them encompass more traditional models that use current and lagged output. In addition, the equations also successfully predict the sharp falls in manufacturing employment that occurred after 1979. One interesting implication…

非高斯状态空间模型及其纪录预测应用AI 解读

SUMMARY We develop a class of state space models for censored data. The basic model assumes an exponential distribution for the observations, conditionally on unobserved state variables. The model may be generalised by allowing transformations. We develop an application to the prediction of records. This is illustrated with some athletics data, though we also discuss briefly the possibility of more general applications connected with extreme values.

贝叶斯模型监测AI 解读

SUMMARY A simple method of monitoring the predictive performance of a class of Bayesian models is introduced. The models involve sequential analyses of sequences of observations and are appropriate for a variety of monitoring and forecasting applications. For any given model, the monitoring technique is based on a comparison of the predictive ability of the model, measured by the observed values of predictive densities, with that of a single alternative. The alternative is…