On the three-step non-Gaussian quasi-maximum likelihood estimation of heavy-tailed double autoregressive models

This note considers a three-step non-Gaussian quasi-maximum likelihood estimation (TS-NGQMLE) of the double autoregressive model with its asymptotics, which improves efficiency of the GQMLE and circumvents inconsistency of the NGQMLE when the innovation is heavy-tailed. Under mild conditions, the estimator not only can achieve consistency and asymptotic normality regardless of density misspecification of the innovation, but also outperforms the existing estimators, such as the GQMLE and the (weighted) least absolute deviation estimator, when the innovation is indeed heavy-tailed.     

Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.     

Estimation in Random Coefficient Autoregressive Models

Abstract. We propose the quasi‐maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.     

Risk-efficient sequential estimation of multivariate random coefficient autoregressive process

A vector-valued autoregressive time series model is considered. The autoregressive coefficients of the model are random with possible dependencies among them. Estimation of the large number of parameters in such models becomes costly with an increase in dimension. A sequential procedure is proposed that promises a significant gain in the sample size thus reduction in the cost of implementation. The procedure is also risk efficient in the sense that as the cost of sampling becomes negligible the asymptotic predictive risk of the proposed procedure reaches the oracle predictive risk corresponding to the best fixed sample size procedure that assumes the values of the nuisance parameters to be known. Extensive simulation results are presented to illustrate the properties of the proposed procedure in a finite sample.     

Empirical likelihood inference for random coefficient INAR(p) process

In this article, we study the empirical likelihood (EL) method for the pth‐order random coefficient integer‐valued autoregressive process. In particular, the limiting distribution of the log EL ratio statistic is established and the confidence regions for the parameter of interest are derived. Also a simulation study is conducted for the evaluation of the developed approach.     

Structure and estimation of a class of nonstationary yet nonexplosive GARCH models

This article considers GARCH(1,1) models in which the time‐varying coefficients are functions of the realizations of an exogenous stochastic process. Time series generated by this model are in general nonstationary. Necessary and sufficient conditions are given for the existence of nonexplosive solutions, and for the existence of moments of these solutions. The asymptotic properties of the quasi‐maximum likelihood estimator are derived under mild assumptions.     

Robust estimation for copula Parameter in SCOMDY models

In this study, we study the robust estimation for the copula parameter in semiparametric copula‐based multivariate dynamic (SCOMDY) models proposed by Chen and Fan (2006). To this end, instead of the pseudo maximum likelihood estimator in Chen and Fan (2006), we use a minimum density power divergence estimator (MDPDE) proposed by Basu et al. (1998). It is shown that the MDPDE is consistent and asymptotically normal under regularity conditions. We compare the performance between the two estimators when outliers exist through a simulation study.     

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].     

On residual empirical processes of GARCH‐SM models: application to conditional symmetry tests

Abstract. Considering the generalized autoregressive conditionally heteroskedastic with stochastic mean (GARCH‐SM) model, we establish in this article the consistency and the weak representation of a functional of its residual empirical process. Based on this result, a symmetry test for GARCH‐SM model is developed. Simulations are given to show the asymptotic behaviour and normality of the test statistic.     

Estimation of surface diffusion coefficient in liquid phase adsorption

An estimation procedure of surface diffusion coefficient, D_s, in liquid phase adsorption was proposed. The procedure is based on a restricted diffusion model, in which D_s is correlated with molecular diffusivity by considering a restriction energy due to an adsorptive interaction between adsorbates and adsorbents. In some adsorption systems, D_s of different adsorbates could be calculated with an error less than about 50% from only one datum of each adsorption equilibrium constant. Irrespective of temperature, the procedure, can be applied for the estimation of D_s even in a wide range of D_s of about 4 orders of magnitude.     

Maximum likelihood estimation of higher‐order integer‐valued autoregressive processes

Abstract. In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher‐order integer‐valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004) , we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) specification with binomial thinning and Poisson innovations, we examine both the asymptotic efficiency and finite sample properties of the ML estimator in relation to the widely used conditional least squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justified, there are substantial gains to be had from using ML especially when the thinning parameters are large.     

Poisson QMLE of Count Time Series Models

Regularity conditions are given for the consistency of the Poisson quasi‐maximum likelihood estimator of the conditional mean parameter of a count time series model. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific integer‐valued autoregressive (INAR) and integer‐valued generalized autoregressive conditional heteroscedasticity (INGARCH) models are considered. Numerical illustrations, Monte Carlo simulations and real data series are provided.     

Asymptotic Properties of Weighted Least Squares Estimation in Weak PARMA Models

The aim of this work is to investigate the asymptotic properties of weighted least squares (WLS) estimation for causal and invertible periodic autoregressive moving average (PARMA) models with uncorrelated but dependent errors. Under mild assumptions, it is shown that the WLS estimators of PARMA models are strongly consistent and asymptotically normal. It extends Thm 3.1 of Basawa and Lund (2001) on least squares estimation of PARMA models with independent errors. It is seen that the asymptotic covariance matrix of the WLS estimators obtained under dependent errors is generally different from that obtained with independent errors. The impact can be dramatic on the standard inference methods based on independent errors when the latter are dependent. Examples and simulation results illustrate the practical relevance of our findings. An application to financial data is also presented.     

Estimation of true biomass energetic yield and maintenance coefficient with errors in the variables

Simple regression analysis is based on the least square of residual errors. To use this method, an independent variable which is observable without error is assumed to exist. For most analyses, the assumption is normally made without realising the implications. The present study demonstrates how parameters may be estimated with the assumption of error in all measurements. Data sets involving batch/fed-batch cultivation of Candida utilis were analysed using a statistical approach with error in variables for the estimation of true biomass energetic yield and maintenance coefficient. This approach requires replicated measurements. For linear systems, such as in chemical/biochemical processes, replicated measurements are sometimes possible or there are physical/physiological reasons to group the observations into clusters. When clusters of replicated measurements are not available, the cluster analysis technique may be used to identify groups of near-replicated data points. The procedure yielded similar results to those obtained by the ordinary least squares procedure for consistent data. However, for data of poor quality (especially data with large errors in the independent variable), the disparity between the two procedures was more significant.     

氨基酸在水溶液中扩散系数的预测

以F ick定律和不可逆过程热力学为基础,结合文献中对扩散系数预测模型的分析,提出了一个氨基酸在水溶液中扩散系数预测的半经验模型。用实验测定的7种氨基酸在水溶液中的扩散系数和一些文献中的扩散系数数据对模型参数进行了拟和。利用该模型对多种氨基酸及尿素在水溶液中的扩散系数进行了计算,计算值与实验值的平均相对误差均小于0.6%。     

用活度系数模型和PCOR状态方程预测聚合物溶液中的亨利常数（英文）

In this paper, the polymer chain of rotator (PCOR) equation of state (EOS) was used together with an EOS/GE mixing rule (MHV1) and the Wilson's equation as an excess-Gibbs-energy model in the proposed approach to extend the capability and improve the accuracy of the PCOR EOS for predicting the Henry's constant of solutions containing polymers. The results of the proposed method compared with two equation of state (van der Waals and GC-Flory) and three activity coefficient models (UNIFAC, UNIFAC-FV and Entropic-FV) indicated that the PCOR EOS/Wilson's equation provided more accurate results. The interaction parameters of Wilson's equation were fitted with Henry's constant experimental data and the property parameters of PCOR, a and b, were fitted with experimental volume data (Tait equation). As a result, the present work provided a simple and useful model for prediction of Henry's constant for polymer solutions.     

Prediction of Henry's constant in polymer solutions using PCOR equation of state coupled with an activity coefficient model

In this paper, the polymer chain of rotator (PCOR) equation of state (EOS) was used together with an EOS/GE mixing rule (MHV1) and the Wilson's equation as an excess-Gibbs-energy model in the proposed ...     

Asymptotics for the Conditional‐Sum‐of‐Squares Estimator in Multivariate Fractional Time‐Series Models

This article proves consistency and asymptotic normality for the conditional‐sum‐of‐squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time‐series models. The model is parametric and quite general and, in particular, encompasses the multivariate non‐cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probability, thus making the proof much more challenging than usual. The neighbourhood around the critical point where uniform convergence fails is handled using a truncation argument.     

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.