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多水平非平稳输入线性系统的盲反卷积AI 解读

A method is proposed to deal with the problem of blind deconvolution of a special non-Gaussian linear process, in which the input to the linear system is a real- or complex-valued multilevel random sequence that satisfies certain regularity conditions. The gist of the method is to apply a linear filter to the observed process and adjust the filter until a multilevel output is obtained. It is shown that the deconvolution problem can be solved (with only scale/rotation and…

自回归分位数及相关秩得分过程AI 解读

This paper develops extensions of the regression quantiles of Koenker and Bassett (1978) to autoregression. It generalizes several results of Jureckova (1992a) and Gutenbrunner and Jureckova (1992) in linear regression to autoregression models. In particular, it gives the asymptotic uniform linearity of linear rank-scores statistics based on residuals suitable in autoregression. It also discusses the two types of $L$-statistics appropriate in autoregression.

对偶似然AI 解读

This paper introduces the concept of dual likelihood as a method of improving accuracy in inference situations depending on martingale estimating equations. Asymptotic results are given for the dual likelihood ratio statistic, and the structure of the family of alternatives is explored. Applications to survival analysis and also to time series, likelihood inference and independent observations are given. Connections to nonparametric likelihood (including empirical…

将定性调查回答转化为定量预期的方法：澳大利亚制造业应用AI 解读

Abstract This paper analyses and extends alternative procedures for converting qualitative expectations responses to quantitative expectations. A number of conversion procedures is investigated, including the probability model, the time‐varying parameter probability model, and the regression approach. The informational content of the survey expectations is compared with simple time series models. It is found that the expectations models are superior for many series, both in…

解释变量非平稳时需求系统的齐次性检验AI 解读

Abstract An implication of optimizing theory is that demand functions are homogeneous of degree zero in prices and nominal income. Evidence based on estimations of demand systems has repeatedly found this restriction to be rejected by the data. However, the hypothesis is often formulated in terms of regressors that are non‐stationary. This paper re‐examines the evidence for homogeneity in light of recent developments in time‐series econometrics with special emphasis on the…

协整系统中的预测AI 解读

Abstract We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I (1) variables in levels, differences, and cointegrated combinations. Asymptotic formulae are obtained for multi‐step forecast error variances for each representation. Alternative measures of forecast accuracy are discussed. Finite sample behaviour in a bivariate model is studied by Monte Carlo using control variables. We also analyse the…

国际产出的收敛AI 解读

Abstract This paper proposes and tests new definitions of convergence and common trends for per capita output. We define convergence for a group of countries to mean that each country has identical long‐run trends, either stochastic or deterministic, while common trends allow for proportionality of the stochastic elements. These definitions lead naturally to the use of cointegration techniques in testing. Using century‐long time series for 15 OECD economies, we reject…

一阶移动平均模型似然函数的闭式表达AI 解读

The covariance matrix of a first order moving average process is expressed as the product of the covariance matrix of the dual autoregressive process of order one and a near identity matrix. Its inverse is then obtained. The closed form of the likelihood function is derived. A comparison is made with some approximate likelihood functions.

多窗谱估计量的有效带宽AI 解读

The bandwidth of a spectral estimator is a measure of the minimum separation in frequency between approximately uncorrelated spectral estimates. We determine an effective bandwidth measure for multitaper spectral estimators, a relatively new and very powerful class of spectral estimators proving to be very valuable whenever the spectrum of interest is detailed and/ or varies rapidly with a large dynamic range. The multitaper spectral estimator is the average of several…

次序统计量之差的鞍点近似AI 解读

Saddlepoint approximations for the distribution of the difference of order statistics from a continuous distribution are presented. The approximations are illustrated for the case of the exponential distribution where we can find the exact probability density function of the difference of order statistics, and for the case of the noncentral chi-squared distribution which has application to the problem of testing a time series for linearity. In both cases we show that the…

平稳向量 ARMA 模型的精确最大似然估计AI 解读

The problems of evaluating and subsequently maximizing the exact likelihood function of vector autoregressive moving average (ARMA) models are considered separately. A new and efficient procedure for evaluating the exact likelihood function is presented. This method puts together a set of useful features that can only be found separately in currently available algorithms. A procedure for maximizing the exact likelihood function, which takes full advantage of the properties…

部分非平稳向量 ARMA 模型的估计与单位根检验AI 解读

The Gaussian estimation of a partially nonstationary autoregressive model and the related issue of testing for cointegration using the likelihood ratio test procedure have been considered by others. In this article we extend the Gaussian estimation procedure to partially nonstationary autoregressive moving average models and derive the asymptotic properties of the full-rank and reduced-rank Gaussian estimators. Based on these results, the asymptotic distribution of the…

计数时间序列模型的蒙特卡洛 EM 估计AI 解读

The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in very complex likelihoods, and even the EM algorithm, which is usually well suited for problems of this type, involves high-dimensional integration. In this article we discuss a Monte Carlo EM (MCEM) algorithm that uses a Markov chain sampling technique in the calculation of the expectation…

基于估计函数的滤波与平滑AI 解读

We consider the problem of filtering and smoothing in state-space models, which include nonlinear and non-Gaussian models. We do not make any distributional assumptions about the processes involved. Our approach to these problems is based on the theory of estimating functions. Filter and smoother are obtained as solutions of estimating equations that are optimal in appropriate classes. We illustrate our procedures by simulation studies of a model where the observational…

ARMA 模型单位根检验中的数据驱动截断滞后选择AI 解读

We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaike's…

部分非平稳向量自回归移动平均模型的估计与单位根检验AI 解读

The Gaussian estimation of a partially nonstationary autoregressive model and the related issue of testing for cointegration using the likelihood ratio test procedure have been considered by others. In this article we extend the Gaussian estimation procedure to partially nonstationary autoregressive moving average models and derive the asymptotic properties of the full-rank and reduced-rank Gaussian estimators. Based on these results, the asymptotic distribution of the…

一阶移动平均模型似然函数的闭式表达AI 解读

The covariance matrix of a first order moving average process is expressed as the product of the covariance matrix of the dual autoregressive process of order one and a near identity matrix. Its inverse is then obtained. The closed form of the likelihood function is derived. A comparison is made with some approximate likelihood functions.

多窗谱估计量的有效带宽AI 解读

The bandwidth of a spectral estimator is a measure of the minimum separation in frequency between approximately uncorrelated spectral estimates. We determine an effective bandwidth measure for multitaper spectral estimators, a relatively new and very powerful class of spectral estimators proving to be very valuable whenever the spectrum of interest is detailed and/ or varies rapidly with a large dynamic range. The multitaper spectral estimator is the average of several…

次序统计量之差的鞍点近似AI 解读

Saddlepoint approximations for the distribution of the difference of order statistics from a continuous distribution are presented. The approximations are illustrated for the case of the exponential distribution where we can find the exact probability density function of the difference of order statistics, and for the case of the noncentral chi-squared distribution which has application to the problem of testing a time series for linearity. In both cases we show that the…

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对异常值稳健的单位根检验及其扩展 Nelson–Plosser 数据应用AI 解读

This paper considers unit root tests based on robust estimators with a high breakdown point and high efficiency. The asymptotic distribution of these tests is derived. Critical values for the test are obtained via simulation. It is found that the size of the classical OLS based Dickey-Fuller test breaks down if the time series contains additive outliers For innovative outliers the size of the robust test is less stable, while its size-adjusted power properties are better. An…

扩展记忆变量之间非线性关系的建模AI 解读

A definition of extended memory is provided, generalizing the ideas of long memory and persistence, based on the properties of forecasts over long horizons. Specification of nonlinear models with variables having extended memory is considered in terms of the balance of an equation and it is suggested that many more types of misspecification can occur than with usual situations and could produce important specification errors. Tests of linearity and standard methods of…