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去趋势、典型事实与经济周期AI 解读

Abstract The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we analyse the consequences of the widely used detrending technique popularised by Hodrick and Prescott (1980). It is shown that mechanical detrending based on the Hodrick–Prescott filter can lead investigators to report spurious cyclical behaviour, and this point is illustrated with empirical examples. Structural time‐series models also…

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Time series sometimes consist of count data in which the number of events occurring in a given time interval is recorded. Such data are necessarily nonnegative integers, and an assumption of a Poisson or negative binomial distribution is often appropriate. This article sets ups a model in which the level of the process generating the observations changes over time. A recursion analogous to the Kalman filter is used to construct the likelihood function and to make predictions…

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宏观经济时间序列中的趋势与周期AI 解读

Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The models are then estimated via the Kalman filter using data on five U.S. macroeconomic time series. The results provide some interesting insights into the dynamic structure of the series, particularly with respect to cyclical behavior. At the same time, they illustrate the development of a model selection strategy for structural time series models.

高阶连续时间自回归模型的估计AI 解读

A method is presented for computing maximum likelihood, or Gaussian, estimators of the structural parameters in a continuous time system of higherorder stochastic differential equations. It is argued that it is computationally efficient in the standard case of exact observations made at equally spaced intervals. Furthermore it can be applied in situations where the observations are at unequally spaced intervals, some observations are missing and/or the endogenous variables…

经济时间序列中缺失观测的估计AI 解读

Two related problems are considered. The first concerns the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation. The second concerns the estimation of the missing observations. Both problems can be solved by setting up the model in state space form and applying the Kalman filter.

时间序列模型中确定性趋势与季节成分的检验AI 解读

A univariate time series model can be set up as the sum of trend, seasonal and irregular components. The trend and seasonal components will normally be stochastic, but deterministic components arise as a special case. This paper develops a test that the trend and seasonal components are deterministic using the approach of Lehmann. The procedure is then extended to test for deterministic components in a model formulated in first differences. Both tests are exact and critical…

用结构模型与 Box–Jenkins 模型预测经济时间序列：案例研究AI 解读

The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series and the forecasting…

《用结构模型与 Box–Jenkins 模型预测经济时间序列：案例研究》答复AI 解读

A. C. Harvey, P. H. J. Todd, [Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study]: Response, Journal of Business & Economic Statistics, Vol. 1, No. 4 (Oct., 1983), pp. 313-315

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时间序列模型中确定性趋势与季节成分的检验AI 解读

A univariate time series model can be set up as the sum of trend, seasonal and irregular components. The trend and seasonal components will normally be stochastic, but deterministic components arise as a special case. This paper develops a test that the trend and seasonal components are deterministic using the approach of Lehmann. The procedure is then extended to test for deterministic components in a model formulated in first differences. Both tests are exact and critical…

季节移动平均模型中缺项检验的一则札记AI 解读

A. C. Harvey, J. Tomenson; A Note on Testing for Gaps in Seasonal Moving Average Models, Journal of the Royal Statistical Society Series B: Statistical Met

季节移动平均模型中滞后缺项检验的一则注记AI 解读

SUMMARY A Lagrange multiplier test is suggested as an alternative to the procedure proposed by Godolphin for testing for gaps in a moving average model. It is shown to have more desirable statistical properties.

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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…

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稳健回归初步估计量的比较AI 解读

The minimum absolute deviation (MAD) estimator may be used as an initial, or preliminary, estimator in a robust regression procedure. Alternative estimators have been developed by Hinich and Talwar (1975) and Andrews (1974). All three estimators are related to the median, and so their respective asymptotic properties can be compared relatively easily, at least in simple models. The conclusion which emerges from such comparisons is that the asymptotic properties of the MAD…

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回归分析中序列相关的简单检验AI 解读

An exact test for serial correlation in regression models is proposed based on the fact that under classical assumptions and including normality for disturbances, the successive quantities are independent N(0, σ2). For various types of data the power of the proposed test compares favorably with that of the BLUS test, but neither test is as powerful as the full Durbin-Watson procedure in which an approximate significance point is calculated when the bounds test is…