Journal of Time Series Analysis

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DETERMINING THE NUMBER OF TERMS IN A TRIGONOMETRIC REGRESSION
Journal of Time Series Analysis - Tập 15 Số 6 - Trang 613-625 - 1994
L. Kavalieris, E. J. Hannan
Abstract. We consider the estimation of the number of sinusoidal terms in a time series contaminated by additive noise with unknown correlation structure. The method fits sinusoidal terms by least squares and models the noise component using a high order autoregression. A criterion based on the minimum description length principle is used to select the number of sinusoidal terms and the order of t... hiện toàn bộ
ESTIMATING THE NUMBER OF TERMS IN A SINUSOIDAL REGRESSION
Journal of Time Series Analysis - Tập 10 Số 1 - Trang 71-75 - 1989
Barry G. Quinn
Abstract. A procedure based on the automatic information criterion procedure of Akaike is presented for estimating the number of sinusoidal terms present in a time series. The procedure is shown to produce a strongly consistent estimator.
Error‐correction Mechanism Tests for Cointegration in a Single‐equation Framework
Journal of Time Series Analysis - Tập 19 Số 3 - Trang 267-283 - 1998
Anindya Banerjee, Juan J. Dolado, Ricardo Mestre
A new test is proposed for cointegration in a single‐equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as an error‐correction mechanism (ECM) test and is based upon the ordinary least squares coefficient of the lagged dependent variable in an autoregressive distributed lag model augmented with leads of the regressors. The limit distrib... hiện toàn bộ
ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS
Journal of Time Series Analysis - Tập 16 Số 4 - Trang 415-429 - 1995
Heon Jin Park, Wayne A. Fuller
Abstract. We compare several estimators for the second‐order autoregressive process and compare the associated tests for a unit root. Monte Carlo results are reported for the ordinary least squares estimator, the simple symmetric least squares estimator and the weighted symmetric least squares estimator. The weighted symmetric least squares estimator of the autoregressive parameters generally has ... hiện toàn bộ
ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON‐LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY
Journal of Time Series Analysis - Tập 15 Số 6 - Trang 627-636 - 1994
Wai Keung Li, Tak K. Mak
Abstract. Time series with a changing conditional variance have been found useful in many applications. Residual autocorrelations from traditional autoregressive moving‐average models have been found useful in model diagnostic checking. By analogy, squared residual autocorrelations from fitted conditional heteroskedastic time series models would be useful in checking the adequacy of such models. I... hiện toàn bộ
TESTING FOR GAUSSIANITY AND LINEARITY OF A STATIONARY TIME SERIES
Journal of Time Series Analysis - Tập 3 Số 3 - Trang 169-176 - 1982
Melvin J. Hinich
Abstract. Stable autoregressive (AR) and autoregressive moving average (ARMA) processes belong to the class of stationary linear time series. A linear time series {} is Gaussian if the distribution of the independent innovations {ε(t)} is normal. Assuming that Eε(t) = 0, some of the third‐order cumulants cxxx=Ex(t)x(t+m)x(t+n) will be non‐zero if the ε(t) are not normal and Eε3(t)≠O. If the relati... hiện toàn bộ
The averaged periodogram estimator for a power law in coherency
Journal of Time Series Analysis - Tập 33 Số 2 - Trang 340-363 - 2012
Rebecca J. Sela, Clifford M. Hurvich
We prove the consistency of the averaged periodogram estimator (APE) in two new cases. First, we prove that the APE is consistent for negative memory parameters, after suitable tapering. Second, we prove that the APE is consistent for a power law in the cross‐spectrum and therefore for a power law in the coherency, provided that sufficiently many frequencies are used in estimation. Simulation evid... hiện toàn bộ
AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
Journal of Time Series Analysis - Tập 3 Số 4 - Trang 253-264 - 1982
Robert H. Shumway, David S. Stoffer
Abstract. An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state‐space model, the EM algorithm is used in conjunction with the conventional Kalman smoothed estimators to derive a simple recursive procedure for estimating the parameters by maximum likelihood. An example is given which involves smoothing and forecasting an economic ser... hiện toàn bộ
Rate of convergence in the central limit theorem for parameter estimation in a causal, invertible ARMA(p, q) model
Journal of Time Series Analysis - Tập 34 Số 1 - Trang 130-137 - 2013
Sugata Sen Roy, Sankha Bhattacharya
In this study we consider the estimators of the parameters of a stable ARMA(p, q) process. The autoregressive parameters are estimated by the instrumental variable technique while the moving average parameters are estimated using a derived autoregressive process. The estimators are shown to be asymptotically normal and their rate of convergence to normality is derived.
MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES
Journal of Time Series Analysis - Tập 17 Số 6 - Trang 571-599 - 1996
Elias Masry
Abstract. Local high‐order polynomial fitting is employed for the estimation of the multivariate regression function m(x1,…xd) =E{φ(Yd)φX1=x1,…,Xd=xd}, and of its partial derivatives, for stationary random processes {Yi, Xi}. The function φ may be selected to yield estimates of the conditional mean, conditional moments and conditional distributions. Uniform strong consistency over compact subsets ... hiện toàn bộ
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