论文检索

利用降维识别大规模时间序列集合中的共享成分AI 解读

In this article we present a framework for parsimonious modeling of large ensembles of time series. The idea is to identify a small number of stochastic time series components such that each series in the ensemble is a weighted sum of series-specific realizations of the components. We present an estimation procedure that is computationally tractable for large datasets. Through simulations, we argue that the estimators perform well. We illustrate the method using two…

室内环境源邻近效应的带标记点过程模型AI 解读

In indoor air quality studies, discrepancies between personal and stationary indoor air quality monitors arise because of the source proximity effect, in which pollutant sources near the respondent cause elevated and highly variable exposures. In a set of experiments in a home, concentrations of a continuously emitting tracer gas were simultaneously monitored at different distances from the tracer gas source. Concentration time series are modeled at collinear monitoring…

使用不截断Bartlett核的异方差与自相关稳健标准误AI 解读

In this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. We show that while such an HAC estimator is not consistent, asymptotically valid testing is still possible. We show that tests using the Bartlett kernel without truncation are exactly equivalent to recent HAC robust tests proposed by Kiefer, Vogelsang and Bunzel (2000, Econometrica, 68, pp 695-714).

This paper presents a new test for fractionally integrated (FI) processes. In particular, we propose a testing procedure in the time domain that extends the well–known Dickey–Fuller approach, originally designed for the I(1) versus I(0) case, to the more general setup of FI(d0) versus FI(d1), with d1

With the cointegration formulation of economic long–run relations the test for cointegrating rank has become a useful econometric tool. The limit distribution of the test is often a poor approximation to the finite sample distribution and it is therefore relevant to derive an approximation to the expectation of the likelihood ratio test for cointegration in the vector autoregressive model in order to improve the finite sample properties. The correction factor depends on…

STAR 与 STAR-GARCH 模型的极大似然估计：理论与蒙特卡洛证据AI 解读

Abstract Theoretical and practical interest in non‐linear time series models, particularly regime switching models, have increased substantially in recent years. Given the abundant research activity in analysing time‐varying volatility through Generalized Autoregressive Conditional Heteroscedasticity (GARCH) processes (see Engle, 1982 ; Bollerslev, 1986 ), it is important to analyse regime switching models with GARCH errors. A popular specification in this class is the…

金融市场波动率动态中的多重结构突变检测AI 解读

Abstract The paper evaluates the performance of several recently proposed tests for structural breaks in the conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data‐driven volatility estimators using high‐frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new tests for detecting breaks in the…

状态空间时间序列分析的简便高效模拟平滑器AI 解读

A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient. The treatment includes models with diffuse initial conditions and regression effects. Computational comparisons are made with the previous standard method. Two applications are provided to illustrate the use…

含缺失值马尔可夫模型中变分似然下界的推断适用性AI 解读

Summary Variational methods have been proposed for obtaining deterministic lower bounds for log-likelihoods within missing data problems, but with little formal justification or investigation of the worth of the lower bound surfaces as tools for inference. We provide, within a general Markovian context, sufficient conditions under which estimators from the variational approximations are asymptotically equivalent to maximum likelihood estimators, and we show empirically, for…

自回归模型中的典型相关分析与降秩回归AI 解读

When the rank of the autoregression matrix is unrestricted, the maximum likelihood estimator under normality is the least squares estimator. When the rank is restricted, the maximum likelihood estimator is composed of the eigenvectors of the effect covariance matrix in the metric of the error covariance matrix corresponding to the largest eigenvalues [Anderson, T. W. (1951). Ann. Math. Statist. 22 327-351]. The asymptotic distribution of these two covariance matrices under…

摘要暂缺

摘要暂缺

核与带宽选择、预白化及完全修正最小二乘的表现AI 解读

This paper examines several practical issues regarding the implementation of the Phillips and Hansen fully modified least squares (FMLS) method for the estimation of a cointegrating vector. Various versions of this method arise by selecting between standard and prewhitened kernel estimation and between parametric and nonparametric automatic bandwidth estimators and also among alternative kernels. A Monte Carlo study is conducted to investigate the finite-sample properties of…

用信息准则选择协整秩与截距形式AI 解读

In this paper we consider a family of penalized likelihood criteria for determining the rank of the cointegration space, the number of lags, and the form of the intercept in vector autoregressions with possibly integrated processes. The paper provides a general consistency result for a class of model determination procedures in which the penalty depends on a simple parameter count only.

分量具有参数联系时的加性模型估计AI 解读

Motivated by a nonparametric GARCH model we consider nonparametric additive autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure is based on two steps. In the first step nonparametric smoothers are used for the estimation of each additive component without taking into account the parametric link of the functions. In a second…

一阶指数 GARCH 模型族的矩结构AI 解读

In this paper we consider the moment structure of a class of first-order exponential generalized autoregressive conditional heteroskedasticity (GARCH) models. This class contains as special cases both the standard exponential GARCH model and the symmetric and asymmetric logarithmic GARCH model. Conditions for the existence of any arbitrary moment are given. Furthermore, the expressions for the kurtosis and the autocorrelations of positive powers of absolute-valued…

线性回归结构变化的最优相似检验AI 解读

This paper analyzes similar tests for structural change for the normal linear regression model in finite samples. Using the approach of Wald (1943, American Mathematical Society Transactions 54, 426–482), Hillier (1987, Econometric Theory 3, 1–44), Andrews and Ploberger (1994, Econometrica 62, 1382–1414), and Andrews, Lee, and Ploberger (1996, Journal of Econometrics 70, 9–36), we characterize a class of optimal similar tests for the existence of (possibly multiple)…

正态变量二次型之比的精确累积分布函数及其 AR(1) 应用AI 解读

Often neither the exact density nor the exact cumulative distribution function (c.d.f.) of a statistic of interest is available in the statistics and econometrics literature (e.g., the maximum likelihood estimator of the autocorrelation coefficient in a simple Gaussian AR(1) model with zero start-up value). In other cases the exact c.d.f. of a statistic of interest is very complicated despite the statistic being “simple” (e.g., the circular serial correlation coefficient, or…

时间序列统计推断的渐近理论AI 解读

Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation…

We show that it is possible to adapt to nonparametric disturbance autocorrelation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency–domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non–normal and has a slow rate of…

删失潜在效应自回归模型及其美国失业应用AI 解读

Abstract A model is proposed to describe observed asymmetries in postwar unemployment time series data. We assume that recession periods, when unemployment increases rapidly, correspond with unobserved positive shocks. The generating mechanism of these latent shocks is a censored regression model, where linear combinations of lagged explanatory variables lead to positive shocks, while otherwise shocks are equal to zero. We apply this censored latent effects autoregression to…

结构向量自回归条件独立图的抽样性质AI 解读

Structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Models of this form, that also have a recursive structure, can be described by a directed acyclic graph. An important tool for identification of these models is the conditional independence graph constructed from the contemporaneous and lagged values of the process. We determine the large‐sample properties of statistics used to test for the…

扩散过程的非参数状态估计AI 解读

The paper presents a method for estimating nonparametrically the states of one‐dimensional diffusion processes. Once certain nuisance parameters have been estimated from the time series, states of a diffusion process can be estimated by the Kalman filter algorithm, so that the method is also useful for filtering and smoothing the states of the process. Numerical comparison of the method with the case of fitting a linear model to data shows that the method is clearly superior…

This paper studies the error in forecasting an autoregressive process with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model that detrends the data using OLS before estimating the autoregressive parameters are much less precise than those based on an autoregression that includes the deterministic components, and the asymptotic distribution of the forecast errors under the two‐step procedure exhibits bimodality.…