Maximum Likelihood Estimation for a First‐Order Bifurcating Autoregressive Process with Exponential Errors

Abstract. Exact and asymptotic distributions of the maximum likelihood estimator of the autoregressive parameter in a first‐order bifurcating autoregressive process with exponential innovations are derived. The limit distributions for the stationary, critical and explosive cases are unified via a single pivot using a random normalization. The pivot is shown to be asymptotically exponential for all values of the autoregressive parameter.     

Testing for a Unit Root in Autoregressive Moving-average Models with Missing Data

Testing for a single autoregressive unit root in an autoregressive moving-average (ARMA) model is considered in the case when data contain missing values. The proposed test statistics are based on an ordinary least squares type estimator of the unit root parameter which is a simple approximation of the one-step Newton–Raphson estimator. The limiting distributions of the test statistics are the same as those of the regression statistics in AR(1) models tabulated by Dickey and Fuller (Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc . 74 (1979), 427–31) for the complete data situation. The tests accommodate models with a fitted intercept and a fitted time trend.     

Testing the Fit of a Vector Autoregressive Moving Average Model

Abstract. A new procedure for testing the fit of multivariate time series model is proposed. The method evaluates in a certain way the closeness of the sample spectral density matrix of the observed process to the spectral density matrix of the parametric model postulated under the null and uses for this purpose nonparametric estimation techniques. The asymptotic distribution of the test statistic is established and an alternative, bootstrap‐based method is developed in order to estimate more accurately this distribution under the null hypothesis. Goodness‐of‐fit diagnostics useful in understanding the test results and identifying sources of model inadequacy are introduced. The applicability of the testing procedure and its capability to detect lacks of fit is demonstrated by means of some real data examples.     

Seasonal Unit Root Tests Based on Forward and Reverse Estimation

In this paper, we suggest a new set of regression-based statistics for testing the seasonal unit root null hypothesis. These tests are based on combining conventional Hylleberg et al . (1990 ) -type seasonal unit root test statistics calculated from both forward and reverse estimation of the auxiliary regression equation. We derive the asymptotic distributions of the new test statistics under the seasonal unit root null hypothesis. We provide finite sample critical values appropriate for the case of quarterly data together with asymptotic critical values, the latter appropriate for any seasonal aspect. Monte Carlo simulation of the finite-sample size and power properties of the new tests reveals that, overall, they perform rather better than extant tests of the seasonal unit root hypothesis.     

Exact Maximum Likelihood Estimation of an ARMA(1, 1) Model with Incomplete Data

For a first-order autoregressive and first-order moving average model with nonconsecutively observed or missing data, the closed form of the exact likelihood function is obtained, and the exact maximum likelihood estimation of parameters is derived in the stationary case.     

Gaussian Maximum Likelihood Estimation For ARMA Models. I. Time Series

Abstract. We provide a direct proof for consistency and asymptotic normality of Gaussian maximum likelihood estimators for causal and invertible autoregressive moving‐average (ARMA) time series models, which were initially established by Hannan [Journal of Applied Probability (1973) vol. 10, pp. 130–145] via the asymptotic properties of a Whittle's estimator. This also paves the way to establish similar results for spatial processes presented in the follow‐up article by Yao and Brockwell [Bernoulli (2006) in press].     

Estimation of the Dominating Frequency for Stationary and Nonstationary Fractional Autoregressive Models

This paper was motivated by the investigation of certain physiological series for premature infants. The question was whether the series exhibit periodic fluctuations with a certain dominating period. The observed series are nonstationary and/or have long-range dependence. The assumed model is a Gaussian process X _t whose m th difference Y_t = (1 − B ) ^m X_t is stationary with a spectral density f that may have a pole (or a zero) at the origin. the problem addressed in this paper is the estimation of the frequency ω_max where f achieves the largest local maximum in the open interval (0, π). The process X_t is assumed to belong to a class of parametric models, characterized by a parameter vector θ, defined in Beran (1995). An estimator of ω_max is proposed and its asymptotic distribution is derived, with θ being estimated by maximum likelihood. In particular, m and a fractional differencing parameter that models long memory are estimated from the data. Model choice is also incorporated. Thus, within the proposed framework, a data driven procedure is obtained that can be applied in situations where the primary interest is in estimating a dominating frequency. A simulation study illustrates the finite sample properties of the method. In particular, for short series, estimation of ω_max is difficult, if the local maximum occurs close to the origin. The results are illustrated by two of the data examples that motivated this research.     

Bootstrap Tests for an Autoregressive Unit Root in the Presence of Weakly Dependent Errors

This paper examines bootstrap tests of the null hypothesis of an autoregressive unit root in models that may include a linear rend and/or an intercept and which are driven by innovations that belong to the class of stationary and invertible linear processes. Our approach makes use of a sieve bootstrap procedure based on residual resampling from autoregressive approximations, the order of which increases with the sample size at a suitable rate. We show that the sieve bootstrap provides asymptotically valid tests of the unit-root hypothesis and demonstrate the small-sample effectiveness of the method by means of simulation.     

A Note on the Information Matrix for Multiplicative Seasonal Autoregressive Moving‐Average Models

Abstract. It is shown that there is an invariance property for each of the elements of the information matrix of a multiplicative seasonal autoregressive moving‐average time‐series model, which enables the integral specification of Whittle (1953a,b) to be solved in a straightforward way. The resulting non‐iterative closed procedure shares the property possessed by the piecemeal approach of Godolphin and Bane (2006) of being independent of the seasonal period, but our procedure is preferable if one or more orders of the seasonal components of the model are greater than unity. The procedure is therefore simpler, in general, than the iterative method of Klein and Mélard (1990) that depends necessarily on the seasonal period. In the strictly non‐seasonal case this invariance property prescribes a non‐iterative closed procedure for evaluating the information matrix which improves on the methods of Godolphin and Unwin (1983) , Friedlander (1984) , McLeod (1984) and Klein and Spreij (2003) . Three illustrations of the approach are given.     

基于时间序列ARMA模型的振动故障预测

运用自回归滑动平均(ARMA)模型和聚类分析方法确定参考样本和故障样本的特征向量,通过特征向量的距离识别故障类型.根据汽轮机典型故障构造模拟信号,建立其ARMA预测模型,通过聚类分析得出标准信号及待测信号的特征向量.经验证,基于ARMA预测模型和聚类分析的方法能够正确识别故障类型.     

Dickey–Fuller, Lagrange Multiplier and Combined Tests for a Unit Root in Autoregressive Time Series

In this paper we investigate (augmented) Dickey–Fuller (DF) and Lagrange multiplier (LM) type unit root tests for autoregressive time series through comprehensive Monte Carlo simulations. We consider two sorts of null and alternative hypotheses: a unit root without drift versus level stationarity and a unit root with drift versus trend stationarity. The DF-type coef ficient tests are found to show the best overall performance in both cases, at least if the sample size is sufficiently large. How ever, it is also found that the DF and LM tests are roughly complementary with regard to their finite-sample power. We therefore consider combining these two types of unit root tests to obtain ( ad hoc 'but') 'robust' test procedures. Critical values for the proposed tests are provided     

Joint Hypothesis Tests for a Unit Root When There is a Break in the Innovation Variance

Abstract. We develop extensions of the Dickey–Fuller F‐statistics for the joint null hypothesis of a unit root that allows for a break in the innovation variance. Our statistics are based on the modified generalized least squares (GLS) strategy outlined in Kim, Leybourne and Newbold [Journal of Econometrics (2002) Vol. 109, pp. 365–387] that requires estimation of the break‐date and corresponding pre‐break and post‐break variances. We derive the asymptotic distribution of the new F‐statistics, tabulate their finite sample and asymptotic critical values, and present finite sample simulation evidence regarding their size and power.     

Quasi‐Maximum Likelihood Estimation for a Class of Continuous‐time Long‐memory Processes

Tsai and Chan (2003) has recently introduced the Continuous‐time Auto‐Regressive Fractionally Integrated Moving‐Average (CARFIMA) models useful for studying long‐memory data. We consider the estimation of the CARFIMA models with discrete‐time data by maximizing the Whittle likelihood. We show that the quasi‐maximum likelihood estimator is asymptotically normal and efficient. Finite‐sample properties of the quasi‐maximum likelihood estimator and those of the exact maximum likelihood estimator are compared by simulations. Simulations suggest that for finite samples, the quasi‐maximum likelihood estimator of the Hurst parameter is less biased but more variable than the exact maximum likelihood estimator. We illustrate the method with a real application.     

Cusum Test for Parameter Change Based on the Maximum Likelihood Estimator

Abstract

In this paper we consider the problem of testing for a parameter change based on the cusum test (cf. Lee, S.; Ha, J.; Na, O.; Na, S. The cusum test for parameter change in time series models. Scand. J. Statist. 2003, 30, 651–739) utilizing the maximum likelihood estimator. The issue is handled in iid random samples, and then special attention is paid to hidden Markov models. It is shown that the limiting distribution of the cusum test statistic is the sup of a standard Brownian bridge under regularity conditions. A simulation result is provided for illustration.     

气体传感器Weibull分布下的一种有效参数估计

通过几种统计方法的比较,选用Weibull分布来对气体传感器的试验数据进行统计分析.针对Weibull分布的参数估计并结合气体传感器试验数据的特殊性,采用威布尔概率纸法和最小二乘法对试验数据进行初步处理,之后利用极大似然估计法对分布参数进行估计.     

Maximum likelihood estimation for the three-parameter Weibull cdf of strength in presence of concurrent flaw populations

For a correct strength characterization of brittle materials, not only the maximum stress at fracture, but also the geometry of the specimens has to be considered thus taking into account the variable stress state and the size effect. Additionally, fracture may occur due to different fracture modes, as for example surface or edge defects. The authors propose a maximum likelihood estimator to obtain the cumulative distribution functions of strength for surface and edge flaw populations separately, both being three-parameter Weibull cdfs referred to an elemental surface area or elemental edge length, respectively. The method has been applied to simulated 3-point bending test data. The estimated Weibull parameters have been used to compute the cdfs of strength for specimens with different size, providing also the confidence bounds calculated by means of the bootstrap method. Finally, fracture data of 4-point bending tests on silicon carbide have been evaluated with the proposed method.     

The Effect of the Estimation on Goodness‐of‐Fit Tests in Time Series Models

Abstract. We analyze, by simulation, the finite‐sample properties of goodness‐of‐fit tests based on residual autocorrelation coefficients (simple and partial) obtained using different estimators frequently used in the analysis of autoregressive moving‐average time‐series models. The estimators considered are unconditional least squares, maximum likelihood and conditional least squares. The results suggest that although the tests based on these estimators are asymptotically equivalent for particular models and parameter values, their sampling properties for samples of the size commonly found in economic applications can differ substantially, because of differences in both finite‐sample estimation efficiencies and residual regeneration methods.     

Estimation and testing for the parameters of ARCH(q) under ordered restriction

Abstract.  In this paper, we study a stationary ARCH( q ) model with parameters α ₀, α ₁, α ₂,…, α _q . It is known that the model requires all parameters α _i to be non-negative, but sometimes the usual algorithm based on Newton–Raphson's method leads us to obtain some negative solutions. So this study proposes a method of computing the maximum likelihood estimator (MLE) of parameters under the non-negative restriction. A similar method is also proposed for the case where the parameters are restricted by a simple order: α ₁≥ α ₂≥⋯≥ α _p . The strong consistency of the above two estimators is discussed. Furthermore, we consider the problem of testing homogeneity of parameters against the simple order restriction. We give the likelihood ratio (LR) test statistic for the testing problem and derive its asymptotic null distribution.     

Impact of the Sampling Rate on the Estimation of the Parameters of Fractional Brownian Motion

Abstract. Fractional Brownian motion is a mean‐zero self‐similar Gaussian process with stationary increments. Its covariance depends on two parameters, the self‐similar parameter H and the variance C. Suppose that one wants to estimate optimally these parameters by using n equally spaced observations. How should these observations be distributed? We show that the spacing of the observations does not affect the estimation of H (this is due to the self‐similarity of the process), but the spacing does affect the estimation of the variance C. For example, if the observations are equally spaced on [0, n] (unit‐spacing), the rate of convergence of the maximum likelihood estimator (MLE) of the variance C is . However, if the observations are equally spaced on [0, 1] (1/n‐spacing), or on [0, n²] (n‐spacing), the rate is slower, . We also determine the optimal choice of the spacing Δ when it is constant, independent of the sample size n. While the rate of convergence of the MLE of C is in this case, irrespective of the value of Δ, the value of the optimal spacing depends on H. It is 1 (unit‐spacing) if H = 1/2 but is very large if H is close to 1.