Recursive method for arma model estimation (I)

In this paper a recursive method is given for estimating model under the natural conditions that the best linear predictor is the best predictor (in the mean square sense). Under these conditions we can prove the estimators ofp ₀ andq ₀ are strongly consistent. The asymptotic normality and the law of iterated logarithm for the estimators of _k 's and _j 's can also be proved.     

ARMA model order and parameter estimation using genetic algorithms

A new method for simultaneously determining the order and the parameters of autoregressive moving average (ARMA) models is presented in this article. Given an ARMA (p, q) model in the absence of any information for the order, the correct order of the model (p, q) as well as the correct parameters will be simultaneously determined using genetic algorithms (GAs). These algorithms simply search the order and the parameter spaces to detect their correct values using the GA operators. The proposed method works on the principle of maximizing the GA fitness value relying on the deviation between the actual plant output, with or without an additive noise, and the estimated plant output. Simulation results show in detail the efficiency of the proposed approach. In addition to that, a practical model identification and parameter estimation is conducted in this article with results obtained as desired. The new method is compared with other well-known methods for ARMA model order and parameter estimation.     

Improved ARMA spectral estimation

The autoregressive (AR) spectral estimator has been studied by several authors, Parzen [10], Burg [3], and Marple [7] to name but a few. Even though the results of Burg and later results of Nuttal [9], Ulrych and Clayton [14] and also Marple [7] significantly improved the AR spectral estimator, it still is somewhat disappointing for narrow band signals or for nearly noninvertible auroregressive moving average (ARMA) data. To circumvent the difficulties, while at the same time introducing a more robust estimator, several authors have suggested the use of the ARMA spectral estimator (e.g. Morton and Gray [8] and Cadzow [4]). In this paper, a new ARMA spectral estimator is introduced which, using a recent result of Tiao and Tsay [12], makes use of dynamic prefiltering. It seems to perform better than previously defined ARMA spectral estimators and the AR spectral estimators of Burg or Marple. Examples are given which include data which is ARMA and data which is not ARMA. Several references to work in this area are included.     

Recursive state estimation for hybrid systems

The paper deals with recursive state estimation for hybrid systems. An unobservable state of such systems is changed both in a continuous and a discrete way. Fast and efficient online estimation of hybrid system state is desired in many application areas. The presented paper proposes to look at this problem via Bayesian filtering in the factorized (decomposed) form. General recursive solution is proposed as the probability density function, updated entry-wise. The paper summarizes general factorized filter specialized for (i) normal state-space models; (ii) multinomial state-space models with discrete observations; and (iii) hybrid systems. Illustrative experiments and comparison with one of the counterparts are provided.     

Recursive estimation and residuals

If observations of a regression relationship have a natural ordering, the estimates of the regression parameters based on the first t observations are called recursive estimates. The paper discusses how to obtain these estimates beginning at t = 0 and the relationship of the changes of the estimates to recursive residuals, which are proportional to the differences between the observation of the response variable at time t and its prediction using information up to time t−1. An extension of these ideas to generalized linear models is suggested.     

Weighted least absolute deviations estimation for periodic ARMA models

This paper investigates the weighted least absolute deviations estimator (WLADE) for causal and invertible periodic autoregressive moving average (PARMA) models. Asymptotic normality of the estimator is derived under a fractional moment condition. A simulation study is given to assess the performance of the proposed WLADE.     

A parameter estimation method for model analysis

To solve a class of nonlinear parameter estimation problems, a method combining the regularized structured nonlinear total least norm (RSNTLN) method and parameter separation scheme is suggested. The method guarantees the convergence of parameters and has an advantages in reducing the residual norm over the use of RSNTLN only. Numerical experiments for two models appeared in signal processing show that the suggested method is more effective in obtaining solution and parameter with minimum residual norm.     

Generalised parameters technique for identification of seasonal ARMA (SARMA) and non seasonal ARMA (NSARMA) models

Times series modeling plays an important role in the field of engineering, Statistics, Biomedicine etc. Model identification is one of crucial steps in the modeling of an AutoRegreesive Moving Average (ARMA(p,q)) process for real world problems. Many techniques have been developed in the literature (Salas et al., McLeod et al. etc.) for the identification of an ARMA(p,q) Model. In this paper, a new technique called The Generalised Parameters Technique is formulated for seasonal and non-seasonal ARMA model identification. This technique is very simple and can be applied to any given time series. Initial estimates of the AR parameters of the ARMA model are also obtained by this method. This model identification technique is validated through many theoretical and simulated examples.     

左删失数据下ARMA(p,q)模型的估计

在观测数据左删失情形下由K—M估计方法得到,严平稳遍历序列{Xt}的均值和自协方差函数的估计,从而获得ARMA（p,q）模型的参数估计,且所给估计量是强相合估计．     

Recursive estimation for continuous time stochastic volatility models

Volatility plays an important role in portfolio management and option pricing. Recently, there has been a growing interest in modeling volatility of the observed process by nonlinear stochastic process [S.J. Taylor, Asset Price Dynamics, Volatility, and Prediction, Princeton University Press, 2005; H. Kawakatsu, Specification and estimation of discrete time quadratic stochastic volatility models, Journal of Empirical Finance 14 (2007) 424–442]. In [H. Gong, A. Thavaneswaran, J. Singh, Filtering for some time series models by using transformation, Math Scientist 33 (2008) 141–147], we have studied the recursive estimates for discrete time stochastic volatility models driven by normal errors. In this paper, we study the recursive estimates for various classes of continuous time nonlinear non-Gaussian stochastic volatility models used for option pricing in finance.     

Recursive parameter estimation: convergence

We consider estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. We propose a wide class of recursive estimation procedures for the general statistical model and study convergence.      

Recursive method for building Arnold’s triangle X p (a,b)

We give a recursive method for building X _p (a,b) for each prime p. Arnold’s triangle is composed of positive integers: for a>1 and 0<b<a, X _p (a,b) is the degree of the highest power of p dividing the difference of the binomial coefficients C _pa ^pb −C _a ^b .      

Recursive parameter estimation: asymptotic expansion

This paper is concerned with the asymptotic behaviour of estimation procedures which are recursive in the sense that each successive estimator is obtained from the previous one by a simple adjustment. The results of the paper can be used to determine the form of the recursive procedure which is expected to have the same asymptotic properties as the corresponding non-recursive one defined as a solution of the corresponding estimating equation. Several examples are given to illustrate the theory, including an application to estimation of parameters in exponential families of Markov processes.     

Two step estimators of the minimum distance type for parameters of the ARMA(1,1) model

New minimum distance estimators are constructed with the help of a preliminary estimator. The asymptotic normality of the constructed estimator is proved with use of a uniform linear expansion of a randomly weighted residual empirical process in a non-standard neighborhood of the true parameter value. Also the question on asymptotic efficiency of the constructed estimator is discussed.     

The exact likelihood for a multivariate ARMA model

A number of algorithms are presented for calculating the exact likelihood of a multivariate ARMA model. There are two aspects to the algorithms. Firstly, the parameterization is in terms of AR parameters and autocovariances. This obviates difficulties with initial MA estimates. Secondly, the algorithms explicitly account for specification of the lag structure of the multivariate time series. Additionally, an algorithm is presented to deal with missing data. The algorithms are, of themselves, not new but they have not been applied to likelihood construction in the manner discussed here.     

Recursive estimation for ordered eigenvectors of symmetric matrix with observation noise

The principal component analysis is to recursively estimate the eigenvectors and the corresponding eigenvalues of a symmetric matrix A based on its noisy observations Ak=A+Nk, where A is allowed to have arbitrary eigenvalues with multiplicity possibly bigger than one. In the paper the recursive algorithms are proposed and their ordered convergence is established: It is shown that the first algorithm a.s. converges to a unit eigenvector corresponding to the largest eigenvalue, the second algorithm a.s. converges to a unit eigenvector corresponding to either the second largest eigenvalue in the case the largest eigenvalue is of single multiplicity or the largest eigenvalue if the multiplicity of the largest eigenvalue is bigger than one, and so on. The convergence rate is also derived.     

Recursive polynomial interpolation algorithm (RPIA)

Two difference schemes are derived for numerically solving the one-dimensional time distributed-order fractional wave equations. It is proved that the schemes are unconditionally stable and convergent in the \(L^{\infty }\) norm with the convergence orders O(τ ² + h ²+Δγ ²) and O(τ ² + h ⁴+Δγ ⁴), respectively, where τ,h, and Δγ are the step sizes in time, space, and distributed order. A numerical example is implemented to confirm the theoretical results.     

Recursive method for building Arnold’s triangle

We prove the propositions stated in Part I [Funct Anal Other Math 1(2):93–109 ([2006])] and give the explicit formulas for X _p (a,b).      

Whittle estimation in a heavy-tailed GARCH(1,1) model

The squares of a GARCH(p,q) process satisfy an ARMA equation with white noise innovations and parameters which are derived from the GARCH model. Moreover, the noise sequence of this ARMA process constitutes a strongly mixing stationary process with geometric rate. These properties suggest to apply classical estimation theory for stationary ARMA processes. We focus on the Whittle estimator for the parameters of the resulting ARMA model. Giraitis and Robinson (2000) show in this context that the Whittle estimator is strongly consistent and asymptotically normal provided the process has finite 8th moment marginal distribution.

We focus on the GARCH(1,1) case when the 8th moment is infinite. This case corresponds to various real-life log-return series of financial data. We show that the Whittle estimator is consistent as long as the 4th moment is finite and inconsistent when the 4th moment is infinite. Moreover, in the finite 4th moment case rates of convergence of the Whittle estimator to the true parameter are the slower, the fatter the tail of the distribution.

These findings are in contrast to ARMA processes with iid innovations. Indeed, in the latter case it was shown by Mikosch et al. (1995) that the rate of convergence of the Whittle estimator to the true parameter is the faster, the fatter the tails of the innovations distribution. Thus the analogy between a squared GARCH process and an ARMA process is misleading insofar that one of the classical estimation techniques, Whittle estimation, does not yield the expected analogy of the asymptotic behavior of the estimators.