论文检索

理性预期假说的计量经济学含义AI 解读

The implications for applied econometrics of the assumption that unobservable expectations formed rationally in Muth's sense examined. The statistical properties of the resulting models and their distributed lag and time series representations described. Purely extrapolative forecasts of endogenous variables can be constructed, as alternatives to rational expectations, but less efficient. Identification and estimation considered: an order condition is that no more…

宏观经济学与现实AI 解读

Existing strategies for econometric analysis related to macroeconomics are subject to a number of serious objections, some recently formulated, some old. These objections are summarized in this paper, and it is argued that taken together they make it unlikely that macroeconomic models are in fact over identified, as the existing statistical theory usually assumes. The implications of this conclusion are explored, and an example of econometric work in a non-standard style,…

时间序列中的隐含周期性自回归移动平均模型AI 解读

Some properties of a class of periodic models for characterizing seasonal time series are explored. The relationships between periodic models and multiple autoregressive-moving average models are developed and used to gain insight into the behaviour of periodic models. In particular it is shown how homogeneous autoregressive-moving average models may be mistakenly specified for series in which periodic properties are present. Consequences of such misspecification on…

平稳时空过程的分析：估计与预测AI 解读

The paper analyses a simultaneous linear model representing stationary spatial-temporal processes. For processes with unit spatial orders, a simple method to compute the likelihood function is developed. Unfortunately, this technique does not generalize to higher order processes. Alternative methods applicable to higher order processes are developed. Finally, it is shown how predictions can be made over time if the parameters are known.

向量自回归模型的渐近预测均方误差AI 解读

This paper derives the asymptotic mean squared error of multistep prediction for the general vector autoregressive process. For one-step-ahead prediction the result is a surprisingly simple generalization of the result for the scalar autoregressive process. Results for multistep prediction are also derived for the regression model with autoregressive errors, where the set of exogenous variables follows a vector autoregressive process.

计量经济模型的统计分析AI 解读

In this article, a summary of some research bearing on the statistical analysis of econometric models is reviewed. Many estimation, testing, and prediction techniques used in econometrics have just large-sample justifications. Selected Bayesian inference results relating to econometric models are reviewed. On the problem of constructing econometric models, an approach that is a blend of traditional econometric and modern time series analysis techniques is described. Many…

贝叶斯稳态预测模型的推广AI 解读

Summary The Bayesian Steady Forecasting model is generalized to a very wide class of processes other than the normal by defining the time series on the decision space. Examples of such processes are presented including a Beta-Binomial process, a Poisson-Gamma process and a Student-t sample distribution steady model. Simple updating relations are given for most of the processes discussed.

基于不精确观测的时间序列预测函数AI 解读

Let $T \subseteq I$ be sets of real numbers. Let $\{Y(t): t\in I\}$ be a real time series whose covariance kernel is assumed known and positive definite. The mean is assumed either to be known or to be an unknown member of a known class of functions on $I$. For each fixed $s\in I, Y(s)$ is predicted by a minimum mean square error unbiased linear predictor $\hat{Y}(s)$ based on $\{Y(t): t\in T\}$. If $\hat{y}(s)$ is the evaluation of $\hat{Y}(s)$ given that the sample path…

时间序列模型设定的综合检验功效研究AI 解读

In this note we present simulation evidence on the power of a portmanteau statistic used to detect time series model misspecification. This is related to the loss in forecasting accuracy resulting from use of the incorrectly specified model. Our conclusion is that the statistic achieves a high level of success only when sample size is large.

摘要暂缺

自回归误差回归模型多步预测的渐近均方误差AI 解读

The asymptotic prediction mean squared error is derived for the regression model with autoregressive errors. The results cover the cases where future values of the exogenous variables are known, where some exogenous variables are predicted from autoregressive processes, and where lagged endogenous variables are included in the model. A result is also obtained for the predictor that ignores autocorrelation of the errors and is based on ordinary least squares (OLS) estimates…

自回归误差回归模型多步预测的渐近均方误差AI 解读

The asymptotic prediction mean squared error is derived for the regression model with autoregressive errors. The results cover the cases where future values of the exogenous variables are known, where some exogenous variables are predicted from autoregressive processes, and where lagged endogenous variables are included in the model. A result is also obtained for the predictor that ignores autocorrelation of the errors and is based on ordinary least squares (OLS) estimates…

摘要暂缺

摘要暂缺

向量自回归模型的渐近预测均方误差AI 解读

This paper derives the asymptotic mean squared error of multistep prediction for the general vector autoregressive process. For one-step-ahead prediction the result is a surprisingly simple generalization of the result for the scalar autoregressive process. Results for multistep prediction are also derived for the regression model with autoregressive errors, where the set of exogenous variables follows a vector autoregressive process.

平稳时空过程分析：估计与预测AI 解读

The paper analyses a simultaneous linear model representing stationary spatial-temporal processes. For processes with unit spatial orders, a simple method to compute the likelihood function is developed. Unfortunately, this technique does not generalize to higher order processes. Alternative methods applicable to higher order processes are developed. Finally, it is shown how predictions can be made over time if the parameters are known.

具有ARMA扰动的回归模型的最大似然估计AI 解读

The regression model with autoregressive-moving average disturbances may be cast in a form suitable for the application of Kalman filtering techniques. This enables the generalized least squares estimator to be calculated without evaluating and inverting the covariance matrix of the disturbances. The problem of forecasting future values of the dependent variable is also effectively solved when the Kalman filter technique is applied. Furthermore, the properties of the…

时间序列模型设定综合检验的若干功效研究AI 解读

In this note we present simulation evidence on the power of a portmanteau statistic used to detect time series model misspecification. This is related to the loss in forecasting accuracy resulting from use of the incorrectly specified model. Our conclusion is that the statistic achieves a high level of success only when sample size is large.

摘要暂缺

平稳时间序列的预测方差及相关统计量AI 解读

A spectral estimate of the generalized prediction variance of a stationary time series is considered and a modification which uses a frequency domain regression to allow for the effect of exogenous variables is introduced. Strong consistency and a central limit theorem are proved for this estimate and for several statistics derived from it. The results of some simulations are presented, together with the results of application to two published sets of data

平稳时间序列的预测方差及相关统计量AI 解读

A spectral estimate of the generalized prediction variance of a stationary time series is considered and a modification which uses a frequency domain regression to allow for the effect of exogenous variables is introduced. Strong consistency and a central limit theorem are proved for this estimate and for several statistics derived from it. The results of some simulations are presented, together with the results of application to two published sets of data

信息有限条件下的预测：平车载拖车运输市场的 ARIMA 模型AI 解读

This paper evaluates the appropriateness of autoregressive integrated moving average (ARIMA) time-series models for forecasting in information-scarce environments. Such environments are defined and a specific transportation sector example examined. It is shown that ARIMA models adequately characterize economic activity in this example. ARIMA model forecasts are seen to dominate alternative models, and to provide information required for market efficiency. Specific attention…

有限信息下的预测：铁路平车载拖车运输市场的 ARIMA 模型AI 解读

This paper evaluates the appropriateness of autoregressive integrated moving average (ARIMA) time-series models for forecasting in information-scarce environments. Such environments are defined and a specific transportation sector example examined. It is shown that ARIMA models adequately characterize economic activity in this example. ARIMA model forecasts are seen to dominate alternative models, and to provide information required for market efficiency. Specific attention…

摘要暂缺