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动态时间序列均值突变检验中非单调功效的来源AI 解读

Sources of nonmonotonic power are uncovered for a wide variety of tests for a shift in the mean of a dynamic time series. Two main sources of nonmonotonic power are found. The first source is the behavior of the estimate of error variance under the alternative hypothesis of a shift in mean. In particular if the error variance is estimated under the null hypothesis, nonmonotonic power can result. The second source is the presence of a lagged dependent variable in the…

This paper examines the roles played by innovations identified from a simple four-variable VAR characterized by cointegration. Using knowledge of cointegration rank and “textbook” relations that link macroeconomic aggregates, we identify distinct “real” and “nominal” innovations that dictate the long-run behavior of the model. We also examine the explanatory power of transitory innovations that are orthogonal to these permanent shocks. One of the permanent shocks displays…

含随机趋势成分回归模型中最大似然估计量的渐近性质AI 解读

This paper investigates the asymptotic properties of the maximum marginal likelihood estimator for a regression model with a stochastic trend component when the signal-to-noise ratio is near zero. In particular, the local level model in Harvey (1989, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press) and its variants where a time trend or an intercept is included are considered. A local-to-zero parameterization is…

单位根原假设下若干检验统计量的渐近矩AI 解读

For three models of linear autoregression the moments of the asymptotic distributions of the test statistics for testing the unit root are obtained in the null case, when the true drift or trend is lacking.

VAR 过程协整秩似然比检验的局部功效AI 解读

Likelihood ratio (LR) tests for the cointegrating rank of a vector autoregressive process have been developed under different assumptions regarding deterministic terms. For instance, nonzero mean terms and linear trends have been accounted for in some of the tests. In this paper we provide a general framework for deriving the local power properties of these tests. Thereby it is possible to assess the virtue of utilizing varying amounts of prior information by making…

基于自适应最大似然估计的单位根检验AI 解读

Adaptive maximum likelihood estimators of unit roots in autoregressive processes with possibly non-Gaussian innovations are considered. Unit root tests based on the adaptive estimators are constructed. Limiting distributions of the test statistics are derived, which are linear combinations of two functionals of Brownian motions. A Monte Carlo simulation reveals that the proposed tests have improved powers over the classical Dickey–Fuller tests when the distribution of the…

基于共轭潜在过程的指数族状态空间模型AI 解读

Summary This paper introduces non-linear and non-Gaussian state space models with analytic updating recursions for filtering and prediction. This new class of models involves some well-known results in the theory of exponential models and of exponential dispersion models and the latent process is defined in such a way that both the filtering and the prediction distributions turn out to be conjugate to the observation distribution at each step. The corresponding analytic and…

通过核回归理解指数平滑AI 解读

Summary Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We…

摘要暂缺

VAR 维度对估计量偏误的影响AI 解读

Vector AutoRegressions (VARs) have now become the most popular tool of Time Series analysis amongst econometricians. Unfortunately, little is known about the analytic finite-sample properties of parameter estimators for such systems. The asymptotic analysis of VARs published to date does not address questions regarding the influence of the number and nature of the system's variates on parameter estimates. Clearly, both questions will have repercussions on the way VARs are…

时间聚合对实证分析的若干影响AI 解读

The author derives the generating mechanism of a temporally aggregated process when the disaggregated one belongs to the vector autoregressive integrated moving average class. He then studies the effects of temporal aggregation on a set of characteristics of usual interest such as exogeneity, causality, cointegration, and common features. An empirical example with Canadian interest rates illustrates the main issues.

有限非平稳时间序列滤波的三种等价方法AI 解读

To estimate the components in an unobserved autoregressive integrated moving average components model, three different approaches can be used--Kalman filtering plus smoothing, Wiener-Kolmogorov filtering, and penalized least squares smoothing. It is shown, in the article, that the three approaches are equivalent. As an application, it is shown that any of the three approaches can be used to filter a series with the Hodrick-Prescott filter but that Wiener-Kolmogorov filtering…

基于对称估计量的双重单位根半参数检验AI 解读

The authors develop new semiparametric tests for double unit roots under a weakly dependent error structure of Phillips for tests for a unit root. The tests are based on symmetric estimation of Sen and Dickey. Through Monte Carlo simulations, the new tests are compared with the tests of Haldrup and of Dickey and Pantula. Their tests have empirical sizes close to the nominal size even when the innovations follow a negatively autocorrelated moving average, for which the…

突发政府干预下的单位根检验：巴西数据应用AI 解读

This paper considers econometric issues related to time-series data that have been subject to abrupt governmental interventions. The motivating example for this study is the Brazilian monthly inflation rate (1974:1-1993:6) which we use throughout for illustration. This series has been heavily influenced by the effect of so-called shock plans implemented by various governments starting in the mid-1980s. The plans act as 'inliers' in the sense that the series is temporarily…

平稳与非平稳短记忆及长记忆自回归过程的统一模型选择AI 解读

The question of model choice for the class of stationary and nonstationary, fractional and nonfractional autoregressive processes is considered. This class is defined by the property that the dth difference, for −½ ≪ d ≪ ∞, is a stationary autoregressive process of order po ≪ ∞. A version of the Akaike information criterion, AIC, for determining an appropriate autoregressive order when d and the autoregressive parameters are estimated simultaneously by a maximum likelihood…

动态回归模型中扰动方差估计量的自由度调整AI 解读

In the classical regression model with fixed regressors the statistic S 2 , i.e. the sum of squared residuals (SSR) divided by the number of degrees of freedom, is an unbiased estimator of the variance of the disturbances. If the model is dynamic and contains lagged-dependent explanatory variables, then the least-squares coefficient estimators are biased in finite samples, and so is S 2 . By deriving the expectation of the initial terms in an expansion of the expression for…

This paper proposes an Augmented Dickey-Fuller (ADF) coefficient test for detecting the presence of a unit root in autoregressive moving average (ARMA) models of unknown order. Although the limit distribution of the coefficient estimate depends on nuisance parameters, a simple transformation can be applied to eliminate the nuisance parameter asymptotically, providing an ADF coefficient test for this case. When the time series has an unknown deterministic trend, we propose a…

自回归模型中单位根检验的分布近似AI 解读

The present work applies saddlepoint approximation to calculate the left-hand tail of the distribution of the unit root t test and an asymptotic equivalent test under the null hypothesis of a unit root. (This is the tail of interest when testing against a stationary alternative.) The saddlepoint equation is solved numerically. Distribution approximations are obtained both in the asymptotic and finite-sample cases. In the finite-sample case, two slightly different methods are…

非平稳时间序列的时变谱分析AI 解读

Modeling of nonstationary stochastic time series has found wide applications in speech processing, biomedical signal processing, seismology, and failure detection. Data from these fields have often been modeled as piecewise stationary processes with abrupt changes, and their time-varying spectral features have been studied with the help of spectrograms. A general class of piecewise locally stationary processes is introduced here that allows both abrupt and smooth changes in…

数值方法下的贝叶斯经济学：利用先验信息开展计量分析与决策AI 解读

Theory and Basics.- A Quick Course in Bayesian Statistics and Decision Theory.- New Advances in Numerical Bayesian Techniques.- Applications in Econometrics.- Imposing Economic Theory.- Studying Parameters of Interest.- Unit Root and Cointegration Tests.- Model Specification Uncertainty.- Forecasting.- More Realistic Models Through Numerical Methods.- Applications to Economic Decision Making.- Decision Theory Applications.

平稳可逆连续时间马尔可夫过程的可嵌入性检验AI 解读

Given an observation of a discrete-time process { Y i , i = 0... n } assumed to be Markov, stationary, and time reversible, we develop a (conservative) test procedure of embeddability by a continuous-time reversible Markov process. The test statistic is derived from a set of moment inequality restrictions implied by the spectral properties of such continuous-time processes. Most interesting is that the embeddability condition of interest is a direct extension of the…

季节自回归模型的矩母函数及进一步精确结果AI 解读

This paper derives the joint moment generating function of quadratic forms occurring in seasonal autoregressive models under stationary, unit root, and explosive specifications. The results are then used to investigate the impact of the seasonal periodicity parameter on various distributional results for both the normalized ordinary least squares coefficient and t -ratio and its effects on the asymptotic bias of parameter estimates.

非参数回归中的偏差校正置信带AI 解读

Summary Bias-corrected confidence bands for general nonparametric regression models are considered. We use local polynomial fitting to construct the confidence bands and combine the cross-validation method and the plug-in method to select the bandwidths. Related asymptotic results are obtained. Our simulations show that confidence bands constructed by local polynomial fitting have much better coverage than those constructed by using the Nadaraya–Watson estimator. The results…