The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
I.v. Basawa - One of the best experts on this subject based on the ideXlab platform.
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first order Random Coefficient integer valued autoregressive processes
Journal of Statistical Planning and Inference, 2007Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:A first-order Random Coefficient integer-valued autoregressive (RCINAR(1)) model is introduced. Ergodicity of the process is established. Moments and autocovariance functions are obtained. Conditional least squares and quasi-likelihood estimators of the model parameters are derived and their asymptotic properties are established. The performance of these estimators is compared with the maximum likelihood estimator via simulation.
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inference for pth order Random Coefficient integer valued autoregressive processes
Journal of Time Series Analysis, 2006Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:. 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.
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Least squares estimation for critical Random Coefficient first-order autoregressive processes
Statistics & Probability Letters, 2006Co-Authors: S.y. Hwang, I.v. Basawa, Tae Yoon KimAbstract:Critical Random Coefficient AR(1) processes are investigated where the Random Coefficient is binary, taking values -1 and 1. Asymptotic behavior of least squares estimator for the mean of the Random Coefficient is discussed. Ordinary least squares estimator is shown to be consistent. Weighted least squares estimator turns out to be asymptotically normally distributed. This enables us to present a unified limit result for the weighted least squares estimator valid for the stationary, explosive and critical cases. Also, a test of criticality is discussed.
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explosive Random Coefficient ar 1 processes and related asymptotics for least squares estimation
Journal of Time Series Analysis, 2005Co-Authors: S.y. Hwang, I.v. BasawaAbstract:Abstract. Large sample properties of the least-squares and weighted least-squares estimates of the autoregressive parameter of the explosive Random-Coefficient AR(1) process are discussed. It is shown that, contrary to the standard AR(1) case, the least-squares estimator is inconsistent whereas the weighted least-squares estimator is consistent and asymptotically normal even when the error process is not necessarily Gaussian. Conditional asymptotics on the event that a certain limiting Random variable is non-zero is also discussed.
Somnath Datta - One of the best experts on this subject based on the ideXlab platform.
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first order Random Coefficient integer valued autoregressive processes
Journal of Statistical Planning and Inference, 2007Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:A first-order Random Coefficient integer-valued autoregressive (RCINAR(1)) model is introduced. Ergodicity of the process is established. Moments and autocovariance functions are obtained. Conditional least squares and quasi-likelihood estimators of the model parameters are derived and their asymptotic properties are established. The performance of these estimators is compared with the maximum likelihood estimator via simulation.
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inference for pth order Random Coefficient integer valued autoregressive processes
Journal of Time Series Analysis, 2006Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:. 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.
Haitao Zheng - One of the best experts on this subject based on the ideXlab platform.
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first order Random Coefficient integer valued autoregressive processes
Journal of Statistical Planning and Inference, 2007Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:A first-order Random Coefficient integer-valued autoregressive (RCINAR(1)) model is introduced. Ergodicity of the process is established. Moments and autocovariance functions are obtained. Conditional least squares and quasi-likelihood estimators of the model parameters are derived and their asymptotic properties are established. The performance of these estimators is compared with the maximum likelihood estimator via simulation.
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inference for pth order Random Coefficient integer valued autoregressive processes
Journal of Time Series Analysis, 2006Co-Authors: Haitao Zheng, I.v. Basawa, Somnath DattaAbstract:. 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.
Lajos Horváth - One of the best experts on this subject based on the ideXlab platform.
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Testing for Randomness in a Random Coefficient autoregression model
Journal of Econometrics, 2019Co-Authors: Lajos Horváth, Lorenzo TrapaniAbstract:Abstract We propose a test to discern between an ordinary autoregressive model, and a Random Coefficient one. To this end, we develop a full-fledged estimation theory for the variances of the idiosyncratic innovation and of the Random Coefficient, based on a two-stage WLS approach. Our results hold irrespective of whether the series is stationary or nonstationary, and, as an immediate result, they afford the construction of a test for ”relevant” Randomness. Further, building on these results, we develop a Randomised test statistic for the null that the Coefficient is non-Random, as opposed to the alternative of a standard R C A ( 1 ) model. Monte Carlo evidence shows that the test has the correct size and very good power for all cases considered.
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statistical inference in a Random Coefficient panel model
Journal of Econometrics, 2016Co-Authors: Lajos Horváth, Lorenzo TrapaniAbstract:This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no “unit root problem” : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive Coefficient is Random or not, and of the presence and degree of cross dependence. Our simulations indicate that the estimator has good properties, and that confidence intervals have the correct coverage even for sample sizes as small as (N,T)=(10,25)(N,T)=(10,25). We illustrate our findings through two applications to macroeconomic and financial variables.
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estimation in nonstationary Random Coefficient autoregressive models
Journal of Time Series Analysis, 2009Co-Authors: István Berkes, Lajos Horváth, Shiqing LingAbstract:We investigate the estimation of parameters in the Random Coefficient autoregressive (RCA) model Xk ¼ (u þ bk)Xk� 1 þ ek, where (u, x 2 , r 2 ) is the parameter of the process, Eb 2 ¼ x 2 , Ee 2 ¼ r 2 . We consider a nonstationary RCA process satisfying E log ju þ b0 j� 0 and show that r 2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimator for (u, x 2 ) is proven so that the unit root problem does not exist in the RCA model.
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Estimation in nonstationary Random Coefficient autoregressive models
arXiv: Methodology, 2009Co-Authors: István Berkes, Lajos Horváth, Shiqing LingAbstract:We investigate the estimation of parameters in the Random Coefficient autoregressive model. We consider a nonstationary RCA process and show that the innovation variance parameter cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimator for the remaining model parameters is proven so the unit root problem does not exist in the Random Coefficient autoregressive model.
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Estimation in Random Coefficient Autoregressive Models
Journal of Time Series Analysis, 2006Co-Authors: Alexander Aue, Lajos Horváth, Josef SteinebachAbstract:. 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.
Aerambamoorthy Thavaneswaran - One of the best experts on this subject based on the ideXlab platform.
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Inference for Random Coefficient volatility models
Statistics & Probability Letters, 2012Co-Authors: Aerambamoorthy Thavaneswaran, You Liang, Julieta FrankAbstract:Abstract Estimating functions have been shown to be convenient to study inference for nonlinear time series models. One such model is the recently proposed Random Coefficient Autoregressive (RCA) model with Generalized Autoregressive Heteroscedasticity (GARCH) errors ( Thavaneswaran et al., 2009 ). We derive the martingale estimating functions for the joint estimation of the conditional mean and variance parameters and we show the information gain relative to conditional least square estimation.
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Random Coefficient volatility models
Statistics & Probability Letters, 2008Co-Authors: Aerambamoorthy Thavaneswaran, Shelton Peiris, Srimantoorao S. AppadooAbstract:Abstract In financial modeling, the moments of the observed process, the kurtosis and the moments of the conditional volatility play important roles. They are very important in model identification and in forecasting the volatility (see Thavaneswaran et al. [(2005b). Forecasting volatility. Statist. Probab. Lett. 75, 1–10.]). This paper introduces Random Coefficient GARCH models including the class Random Coefficient GARCH (RC-GARCH) models and derive their higher order moments and kurtosis.
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Random Coefficient GARCH models
Mathematical and Computer Modelling, 2005Co-Authors: Aerambamoorthy Thavaneswaran, Srimantoorao S. Appadoo, M. SamantaAbstract:Both volatility clustering and conditional nonormality can induce the leptokurtosis typically observed in financial data. An ARMA representation is used to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and Random Coefficient GARCH. Formula for autocorrelations of the power GARCH process |yt|^@d are given in terms of @j-weights. The kurtosis is also derived for Random Coefficient GARCH, nonstationary GARCH with possibly nonnormal errors and for hidden Markov GARCH models. The theoretical autocorrelation functions for various GARCH(1,1) models are also derived.