The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Jingjing Yang - One of the best experts on this subject based on the ideXlab platform.
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Exactly/Nearly Unbiased Estimation of Autocovariances of a Univariate Time Series With Unknown Mean
Journal of Time Series Analysis, 2016Co-Authors: Timothy J. Vogelsang, Jingjing YangAbstract:This article proposes an exactly/nearly unbiased estimator of the autocovariance function of a univariate time series with unknown mean. The estimator is a linear function of the usual sample Autocovariances computed using the observed demeaned data. The idea is to stack the usual sample Autocovariances into a vector and show that the expectation of this vector is a linear combination of population Autocovariances. A matrix that we label, A, collects the weights in these linear combinations. When the population Autocovariances of high lags are zero (small), exactly (nearly) unbiased estimators of the remaining Autocovariances can be obtained using the inverse of upper blocks of the A matrix. The A-matrix estimators are shown to be asymptotically equivalent to the usual sample autocovariance estimators. The A-matrix estimators can be used to construct estimators of the autocorrelation function that have less bias than the usual estimators. Simulations show that the A-matrix estimators can substantially reduce bias while not necessarily increasing mean square error. More powerful tests for the null hypothesis of white noise are obtained using the A-matrix estimators.
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exactly nearly unbiased estimation of Autocovariances of a univariate time series with unknown mean
Journal of Time Series Analysis, 2016Co-Authors: Timothy J. Vogelsang, Jingjing YangAbstract:This article proposes an exactly/nearly unbiased estimator of the autocovariance function of a univariate time series with unknown mean. The estimator is a linear function of the usual sample Autocovariances computed using the observed demeaned data. The idea is to stack the usual sample Autocovariances into a vector and show that the expectation of this vector is a linear combination of population Autocovariances. A matrix that we label, A, collects the weights in these linear combinations. When the population Autocovariances of high lags are zero (small), exactly (nearly) unbiased estimators of the remaining Autocovariances can be obtained using the inverse of upper blocks of the A matrix. The A-matrix estimators are shown to be asymptotically equivalent to the usual sample autocovariance estimators. The A-matrix estimators can be used to construct estimators of the autocorrelation function that have less bias than the usual estimators. Simulations show that the A-matrix estimators can substantially reduce bias while not necessarily increasing mean square error. More powerful tests for the null hypothesis of white noise are obtained using the A-matrix estimators.
Timothy J. Vogelsang - One of the best experts on this subject based on the ideXlab platform.
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Exactly/Nearly Unbiased Estimation of Autocovariances of a Univariate Time Series With Unknown Mean
Journal of Time Series Analysis, 2016Co-Authors: Timothy J. Vogelsang, Jingjing YangAbstract:This article proposes an exactly/nearly unbiased estimator of the autocovariance function of a univariate time series with unknown mean. The estimator is a linear function of the usual sample Autocovariances computed using the observed demeaned data. The idea is to stack the usual sample Autocovariances into a vector and show that the expectation of this vector is a linear combination of population Autocovariances. A matrix that we label, A, collects the weights in these linear combinations. When the population Autocovariances of high lags are zero (small), exactly (nearly) unbiased estimators of the remaining Autocovariances can be obtained using the inverse of upper blocks of the A matrix. The A-matrix estimators are shown to be asymptotically equivalent to the usual sample autocovariance estimators. The A-matrix estimators can be used to construct estimators of the autocorrelation function that have less bias than the usual estimators. Simulations show that the A-matrix estimators can substantially reduce bias while not necessarily increasing mean square error. More powerful tests for the null hypothesis of white noise are obtained using the A-matrix estimators.
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exactly nearly unbiased estimation of Autocovariances of a univariate time series with unknown mean
Journal of Time Series Analysis, 2016Co-Authors: Timothy J. Vogelsang, Jingjing YangAbstract:This article proposes an exactly/nearly unbiased estimator of the autocovariance function of a univariate time series with unknown mean. The estimator is a linear function of the usual sample Autocovariances computed using the observed demeaned data. The idea is to stack the usual sample Autocovariances into a vector and show that the expectation of this vector is a linear combination of population Autocovariances. A matrix that we label, A, collects the weights in these linear combinations. When the population Autocovariances of high lags are zero (small), exactly (nearly) unbiased estimators of the remaining Autocovariances can be obtained using the inverse of upper blocks of the A matrix. The A-matrix estimators are shown to be asymptotically equivalent to the usual sample autocovariance estimators. The A-matrix estimators can be used to construct estimators of the autocorrelation function that have less bias than the usual estimators. Simulations show that the A-matrix estimators can substantially reduce bias while not necessarily increasing mean square error. More powerful tests for the null hypothesis of white noise are obtained using the A-matrix estimators.
Tucker Mcelroy - One of the best experts on this subject based on the ideXlab platform.
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Computation of vector ARMA Autocovariances
Statistics & Probability Letters, 2017Co-Authors: Tucker McelroyAbstract:This note describes an algorithm for computing the autocovariance sequence of a VARMA process, without requiring the intermediary step of determining the Wold representation. Although the recursive formula for the Autocovariances is well-known, the initialization of this recursion in standard treatments (such as Brockwell and Davis (1991) or Lutkepohl (2007)) is slightly nuanced; we provide explicit formulas and algorithms for the initial Autocovariances.
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computation of the Autocovariances for time series with multiple long range persistencies
Computational Statistics & Data Analysis, 2016Co-Authors: Tucker Mcelroy, Scott H HolanAbstract:Gegenbauer processes allow for flexible and convenient modeling of time series data with multiple spectral peaks, where the qualitative description of these peaks is via the concept of cyclical long-range dependence. The Gegenbauer class is extensive, including ARFIMA, seasonal ARFIMA, and GARMA processes as special cases. Model estimation is challenging for Gegenbauer processes when multiple zeros and poles occur in the spectral density, because the autocovariance function is laborious to compute. The method of splitting-essentially computing Autocovariances by convolving long memory and short memory dynamics-is only tractable when a single long memory pole exists. An additive decomposition of the spectrum into a sum of spectra is proposed, where each summand has a single singularity, so that a computationally efficient splitting method can be applied to each term and then aggregated. This approach differs from handling all the poles in the spectral density at once, via an analysis of truncation error. The proposed technique allows for fast estimation of time series with multiple long-range dependences, which is illustrated numerically and through several case-studies.
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subsampling inference for the Autocovariances and autocorrelations of long memory heavy tailed linear time series
Journal of Time Series Analysis, 2012Co-Authors: Tucker Mcelroy, Agnieszka JachAbstract:We provide a self-normalization for the sample Autocovariances and autocorrelations of a linear, long-memory time series with innovations that have either finite fourth moment or are heavy-tailed with tail index 2 < α < 4. In the asymptotic distribution of the sample autocovariance there are three rates of convergence that depend on the interplay between the memory parameter d and α, and which consequently lead to three different limit distributions; for the sample autocorrelation the limit distribution only depends on d. We introduce a self-normalized sample autocovariance statistic, which is computable without knowledge of α or d (or their relationship), and which converges to a non-degenerate distribution. We also treat self-normalization of the autocorrelations. The sampling distributions can then be approximated non-parametrically by subsampling, as the corresponding asymptotic distribution is still parameter-dependent. The subsampling-based confidence intervals for the process Autocovariances and autocorrelations are shown to have satisfactory empirical coverage rates in a simulation study. The impact of subsampling block size on the coverage is assessed. The methodology is further applied to the log-squared returns of Merck stock.
Ryo Okui - One of the best experts on this subject based on the ideXlab platform.
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Misspecification in Dynamic Panel Data Models and Model-Free Inferences
The Japanese Economic Review, 2017Co-Authors: Ryo OkuiAbstract:This paper discusses the issue of model misspecification and model-free methods in dynamic panel data analysis. We primarily review existing results, but also provide several new results. When the dynamics are homogeneous, we show that several widely used estimators for panel first-order autoregressive AR(1) models converge to first-order autocorrelation, even under misspecification. Under heterogeneity, these estimators converge to the ratio of the means of the first-order Autocovariances and variances. We also discuss the estimation of Autocovariances, the estimation of panel AR(∞) models, and the estimation of the distribution of the heterogeneous mean and Autocovariances.
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asymptotically unbiased estimation of Autocovariances and autocorrelations with panel data in the presence of individual and time effects
Journal of Time Series Econometrics, 2014Co-Authors: Ryo OkuiAbstract:This paper proposes asymptotically unbiased estimators of Autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffer from the bias caused by the elimination of individual and time effects. The bias related to individual effects is proportional to the long-run variance, and that related to time effects is proportional to the value of the estimated autocovariance. On the other hand, the elimination of time effects does not cause a bias for the conventional autocorrelation estimators while the elimination of individual effects does. We develop methods to estimate the long-run variance and propose bias- corrected estimators based on the proposed long-run variance estimator. The theoretical results are given by employing double asymptotics under which both the number of observations and the length of the time series tend to infinity. Monte Carlo simulations show that the asymptotic theory provides a good approximation to the actual bias and that the proposed bias correction works.
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asymptotically unbiased estimation of Autocovariances and autocorrelations with panel data in the presence of individual and time effects
Social Science Research Network, 2011Co-Authors: Ryo OkuiAbstract:This paper proposes asymptotically unbiased estimators of Autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffers from the bias caused by the elimination of individual and time effects. The bias related to individual effects is proportional to the long-run variance, and that related to time effects is proportional to the value of the estimated autocovariance. For the conventional autocorrelation estimators, the elimination of time effects does not cause a bias while the elimination of individual effects does. We develop methods to estimate the long-run variance and propose bias-corrected estimators based on the proposed long-run variance estimator. The theoretical results are given by employing double asymptotics under which both the number of observations and the length of the time series tend to infinity. Monte Carlo simulations show that the asymptotic theory provides a good approximation to the actual bias and that the proposed bias correction works.
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Asymptotically unbiased estimation of Autocovariances and autocorrelations for panel data with incidental trends
Economics Letters, 2011Co-Authors: Ryo OkuiAbstract:Abstract We consider the estimation of Autocovariances using panel data with incidental trends under double asymptotics. The conventional autocovariance estimator suffers from a bias whose value is approximated by twice the long-run variance. We propose a bias-corrected estimator.
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efficient estimation of Autocovariances for panel data with individual effects under cross section and time series
2011Co-Authors: Haruo Iwakura, Ryo OkuiAbstract:This paper studies asymptotic efficiency of autocovariance estimation in panel data set- tings with individual effects when both the cross-sectional sample size and the length of time series tend to infinity. The efficiency bound for regular estimators of Autocovariances is de- rived by using a Hajek (1970)-type convolution theorem. In view of the efficiency bound, we provide a necessary and sufficient condition under which bias-corrected autocovariance esti- mators developed by Okui (2010) are asymptotically efficient. In particular, we show that, when the individual dynamics follow an ARMA(p, q) process, the bias-corrected autocovari- ance estimator at lag k is asymptotically efficient if and only if p ‚ q and 0 • kp i q. These efficiency results are analogous to those for time series analysis obtained by Porat (1987) and Kakizawa and Taniguchi (1994).
Benjamin Kedem - One of the best experts on this subject based on the ideXlab platform.
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A note on autocovariance estimation in the presence of discrete spectra
Statistics & Probability Letters, 1995Co-Authors: Christian Houdre, Benjamin KedemAbstract:We provide a necessary and sufficient condition for the almost sure convergence and the strong consistency of the sample autocovariance of a discrete spectrum weakly stationary process. This also clarifies the estimation of the autocovariance function of a mixed spectrum weakly stationary process.
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On Autocovariance Estimation for Discrete Spectrum Stationary Time Series
1993Co-Authors: Christian Houdre, Benjamin KedemAbstract:Abstract : We provide a necessary and sufficient condition for the almost sure convergence and the strong consistency of the sample autocovariance of a discrete spectrum weakly stationary process. This also clarifies the estimation of the autocovariance function of a mixed spectrum weakly stationary processes.
