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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…

周期性抽样调查中的无应答AI 解读

A parametric model is developed for analyzing the updated historical (ratio of identicals) imputation strategy in a periodic survey. A test is then developed for comparing alternative formulations of the historical values. The benefits of exponentially smoothing in this context are demonstrated using monthly gasoline sales data.

移动平均季节调整方法中趋势—周期估计量的修订AI 解读

One of the main purposes of the seasonal adjustment of economic time series is to provide information on current economic conditions, particularly to determine the state of the cycle at which the economy stands. Since seasonal adjustment means removing seasonal variations, thus leaving a seasonally adjusted series consisting of trend cycle together with the irregular fluctuations, it is often very difficult to detect cyclical turning points for series strongly contaminated…

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…

高阶样本累积量谱泛函的极限行为AI 解读

This paper is concerned with establishing a broad class of estimators (and the limiting behavior, thereof) for parametrizations of higher than second-order structure. This includes parametrizations which reflect, for example, such properties as nonlinearity and/or non-Gaussianity and/or time irreversibility. Asymptotic distributions, almost-sure convergence, and probability-one bounds for such estimators are established. Several applications of such estimators are discussed.

平稳 ARMA 过程中的自适应估计AI 解读

We consider the estimation problem for the parameter $\vartheta_0$ of a stationary ARMA $(p, q)$ process, with independent and identically, but not necessary normally distributed errors. First we prove local asymptotic normality (LAN) for this model. Then we construct locally asymptotically minimax (LAM) estimators, which asymptotically achieve the smallest possible covariance matrix. Utilizing these, we finally obtain strongly adaptive estimators, by using usual kernel…

非平稳分类时间序列回归模型：渐近估计理论AI 解读

For the analysis of nonstationary categorical time series, a parsimonious and flexible class of models is proposed. These models are generalizations of regression models for stochastically independent categorical observations. Consistency, asymptotic normality and efficiency of the maximum likelihood estimator are shown under weak and easily verifiable requirements. Some models for binary time series are discussed in detail. To demonstrate asymptotic properties, a theorem is…

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.

稳健谱回归AI 解读

This paper addresses the problem of linear regression estimation when the disturbances follow a stationary process with its spectral density known only to be in a neighborhood of some specified spectral density, for instance, that of white noise. Rather than trying to adapt to a small unspecified autocorrelation, we follow here the robustness approach, and establish the extent of the regressors and disturbance spectra interaction which require serial correlation correction.…

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…

自回归谱估计中的谱峰丢失AI 解读

Autoregressive spectral analysis depends on the method used for estimating the autoregressive parameters. It is shown by an asymptotic analysis involving second-order terms that least-squares estimates should be preferred to Yule-Walker estimates, since Yule-Walker estimates may result in loss of spectral peaks and strong bias. The results are confirmed by simulations which include also Burg-type estimates.

状态空间模型的高效广义交叉验证AI 解读

For a signal plus noise model with a state space representation, an efficient procedure is given for obtaining the trace of the influence matrix, where the influence matrix expresses the estimated signal vector as a linear combination of the observed data. This allows an O(n) evaluation of the generalized cross-validation criterion function. Our approach is very efficient, requiring the addition of only one equation to the ordinary Kalman filter, and extends to models with…

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…

非连续观测时间序列参数估计量的渐近分布AI 解读

The asymptotic normal distributions of two estimators, the maximum likelihood estimator and an alternative weighted least-squares estimator proposed by Wincek & Reinsel (1986) for parameter estimation of autoregressive-moving average models with nonconsecutively observed or missing data are derived for the stationary first-order autoregressive model, and also for the moving average model under periodic A-B sampling. Numerical comparisons of the explicit asymptotic variances…

单一变点的检验AI 解读

The problem considered is that of testing a sequence of independent normal random variables with constant, known or unknown, variance for no change in mean versus alternatives with a single change-point. Various tests, such as those based on the likelihood ratio and recursive residuals, are studied. Power approximations are developed by integrating approximations for conditional boundary crossing probabilities. A comparison of several tests is made.

均值突变模型中的条件自助法AI 解读

Bootstrap methods are not inherently conditional, but they can be made so by appropriate stratification of the simulated samples which bootstrap produces. We show how stratification can work in a bootstrap analysis of mean-shift in Nile river flow data. The results are compared with both parametric and semiparametric likelihood analyses. The paper ends with some general remarks on conditional bootstraps.

含单位根的时间序列回归AI 解读

This paper studies the random walk in a general time series setting that allows for weakly dependent and heterogeneously distributed innovations. It is shown that simple least squares regression consistently estimates a unit root under very general conditions in spite of the presence of autocorrelated errors. The limiting distribution of the standardized estimator and the associated regression t statistic are found using functional central limit theory. New tests of the…

协整与误差修正：表示、估计与检验AI 解读

The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a…

摘要暂缺

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.

弱误差假设下含单整或动态回归量的最小二乘回归AI 解读

This paper establishes consistency of least squares estimators in (i) a multiple regression model with integrated regressors and explosive, non-mixing errors, and (ii) a dynamic linear regression model with regressors and errors that may have infinite variances. In the former context, the asymptotic distribution of the least squares estimator also is obtained, in certain cases.

混合存量与流量观测下一阶线性连续时间系统的精确离散对应模型AI 解读

This article deals with the derivation of the exact discrete model that corresponds to a closed linear first-order continuous-time system with mixed stock and flow data. This exact discrete model is (under appropriate additional conditions) a stationary autoregressive moving average time series model and may allow one to obtain asymptotically efficient estimators of the parameters describing the continuous-time system.

非平稳向量自回归中的渐近展开AI 解读

This paper studies the statistical properties of vector autoregressions (VAR's) for quite general multiple time series which are integrated processes of order one. Functional central limit theorems are given for multivariate partial sums of weakly dependent innovations and these are applied to yield first-order asymptotics in nonstationary VAR's. Characteristic and cumulant functionals for generalized random processes are introduced as a means of developing a refinement of…