Abstract Autoregressive Research Articles

Structural breaks in Box-Cox transforms of realized volatility: a model selection perspective

Autoregressive (AR) models such as the heterogeneous autoregressive (HAR) model capture the linear footprint inherent in realized volatility. We draw upon the fact that the HAR model is a constrained AR model and cast the problem of estimating structural breaks in the autoregressive volatility dynamics as a model selection problem. A two-step Lasso-type procedure is used to consistently estimate the unknown number and timing of structural breaks. Empirically, we find the number of breaks to be heavily influenced by Box-Cox transformations applied to realized volatility series of eight stock market indices: For example, while we find breaks in the original series, no breaks are found in log-realized volatility, a measure often used in applied research, across a wide range of lag lengths. These Box-Cox transformations lead to different volatility processes with distinct autoregressive dynamics and affect the estimation of structural breaks. Importantly, the log-transformation considerably reduces the number of price jumps which might otherwise be selected as structural breaks.

Analysis of adequacy of autoregressive model for Lithuanian vowels

Autoregressive (AR) model is widely used for modeling of speech signals. Nevertheless, the problem of adequate modelling of Lithuanian speech is still open. The results of this study indicate the need of much higher order models for Lithuanian wovel description. Only high order AR models enable us to model frequential properties of Lithuanian vowels adequately.

Optimal filtering of multivariate noisy AR processes

Autoregressive (AR) models play a role of paramount importance in the description of scalar and multivariate time series and find many applications in prediction and filtering. The main limit of AR models is associated with their elementary description of the misfit between observations and model (equation error considered as a white noise). A more realistic family of autoregressive models is given by “AR+noise” ones where besides a white equation error also additive white noise on the observations is considered. Noisy AR models have given very good results in practical applications and lead to more realistic descriptions of the underlying processes; for these reasons, they are intrinsically more suitable than AR models for filtering applications. The use of AR+noise models in filtering requires the construction of a state-space realization and Kalman filtering. This article proposes an efficient innovation-based filtering approach whose computational burden is lower than that of Kalman filtering. The proposed algorithm relies directly on polynomial input–output models and on Cholesky factorization.

Wind-Induced Vibration Response Analysis of a High-Rise Structure

Autoregressive (AR) method can provide a simulation of random process with relatively short computational time and acceptable accuracy while Newmark-β Method is a quick way to accomplish the response analysis. Therefore in this paper, the combination of these two methods will solve the problem of wind-induced response analysis quickly and precisely. According to several theories, such as wind engineering, vibration theory and random process, we succeed to model 37-dimensional correlated random wind velocity and it is used for the response analysis. Then we utilize Newmark-β method to analyze successfully and the key results demonstrate that Guangzhou New TV Tower does not satisfy the requirements in the building code of China thus vibration control is needed.

A new coefficient estimation method for autoregressive systems using cumulants

AbstractAutoregressive (AR) system identification with only output measurements is a well‐known problem in various science and engineering areas such as spectral estimation and speech processing. This paper addresses the problem of estimating the coefficients of an AR model from third‐order cumulants of the noisy observations of the system output. The system is driven by a zero‐mean independent and identically distributed (i.i.d) non‐Gaussian sequence. The input sequence is not observed. Simulation results are presented that demonstrate the performance of the new approach and compare it with a recently developed technique. Copyright © 2001 John Wiley & Sons, Ltd.

AR and ARMA spectral estimation

Autoregressive (AR) and Autoregressive-moving average (ARMA) methods of spectral analysis have been developed and are being increasingly used as alternatives to traditional methods of spectral analysis. Two of these methods developed by Marple and Friedlander are tested in this study by using generated data from models with known spectra. The Blackman-Tukey spectral estimates are also compared to the Marple and Friedlander estimates. The variability of the Marple and Friedlander estimates with sample sizes is investigated. Although both Marple's and Friedlander's methods are satisfactory, Friedlander's method is preferred because of its ability to handle a wider class of models.