The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
I. G. Erlikh - One of the best experts on this subject based on the ideXlab platform.
-
Testing hypotheses on the “drift” of parameters in ARMA and ARCH models
Mathematical Methods of Statistics, 2009Co-Authors: M. V. Boldin, I. G. ErlikhAbstract:For an ARMA model, we test the hypothesis that the coefficients of this model remain constant in time and satisfy the Stationarity Condition against the alternative that the coefficients change (“drift”) in time. We propose asymptotically distribution free tests for such hypothesis based on sequential residual processes. A similar problem is solved for the ARCH model.
Christian Gourieroux - One of the best experts on this subject based on the ideXlab platform.
-
Negative Binomial Autoregressive Process with Stochastic Intensity
Journal of Time Series Analysis, 2018Co-Authors: Christian Gourieroux, Yang LuAbstract:We introduce negative binomial‐60 autoregressive (NBAR) processes with stochastic intensity for (univariate and bivariate) count processes. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial Conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are compound autoregressive, which allows for simple Stationarity Condition and quasi‐closed form nonlinear forecasting formulae at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.
-
Negative Binomial Autoregressive Process
2018Co-Authors: Yang Lu, Christian GourierouxAbstract:We introduce Negative Binomial Autoregressive (NBAR) processes for (univariate and bivariate) count time series. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial Conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are Compound Autoregressive, which allows for simple Stationarity Condition and quasi-closed form nonlinear forecasting formulas at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.
Yang Lu - One of the best experts on this subject based on the ideXlab platform.
-
Negative Binomial Autoregressive Process with Stochastic Intensity
Journal of Time Series Analysis, 2018Co-Authors: Christian Gourieroux, Yang LuAbstract:We introduce negative binomial‐60 autoregressive (NBAR) processes with stochastic intensity for (univariate and bivariate) count processes. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial Conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are compound autoregressive, which allows for simple Stationarity Condition and quasi‐closed form nonlinear forecasting formulae at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.
-
Negative Binomial Autoregressive Process
2018Co-Authors: Yang Lu, Christian GourierouxAbstract:We introduce Negative Binomial Autoregressive (NBAR) processes for (univariate and bivariate) count time series. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial Conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are Compound Autoregressive, which allows for simple Stationarity Condition and quasi-closed form nonlinear forecasting formulas at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.
Subanar Subanar - One of the best experts on this subject based on the ideXlab platform.
-
some comments on the theorem providing Stationarity Condition for gstar models in the paper by borovkova et al
Journal of the Indonesian Mathematical Society, 2007Co-Authors: Suhartono Suhartono, Subanar SubanarAbstract:Generalized Space-Time Autoregressive (GSTAR) model is one of the models that usually used for modeling and forecasting space and time series data. The aim of this paper is to study further about the Stationarity Conditions for parameters in the GSTAR model and the relation to Vector Autoregressive (VAR) model. We focus on the theoretical study about Stationarity Condition in GSTAR(11) and the relation tothe Stationarity Condition of parameters in VAR(1). Then, we do an empirical study to give counter examples for the theorem of Stationarity Condition proposed by Borovkovaet al. The results show that the theorem of Stationarity Condition of parameters in GSTAR(11) model given by Borovkova et al. is incorrect. Additionally, the empirical results also show that GSTAR(11) model could always be represented in VAR(1) model by applying matrix operation to the space and time parameters. Hence, we can also conclude that VAR model, particularly VAR(1), is an extension of GSTAR(11) model with any possibility values of space and time parameters. DOI : http://dx.doi.org/10.22342/jims.13.1.90.115-122
Kalyan B. Sinha - One of the best experts on this subject based on the ideXlab platform.
-
CHARACTERIZATION OF UNITARY PROCESSES WITH INDEPENDENT INCREMENTS
Communications on Stochastic Analysis, 2010Co-Authors: Un Cig Ji, Lingaraj Sahu, Kalyan B. SinhaAbstract:In this paper, we study unitary Gaussian processes with independent in- crements with which the unitary equivalence to a Hudson-Parthasarathy evolution systems is proved. This gives a generalization of results in (16) and (17) in the absence of the Stationarity Condition.
-
Unitary Processes with Independent Increments
arXiv: Functional Analysis, 2010Co-Authors: Un Cig Ji, Lingaraj Sahu, Kalyan B. SinhaAbstract:In this paper, we study unitary Gaussian processes with independent increments with which the unitary equivalence to a Hudson-Parthasarathy evolution systems is proved. This gives a generalization of results in [16] and [17] in the absence of the Stationarity Condition.