random coefficient autoregressive models
Bayesian analysis of random coefficient autoregressive models
... RCAR models are robust to model misspecification and the loss in efficiency due to more parameters is not severe as compared to AR ...RCAR models to time series data where volatility is ...
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Frequentist and Bayesian Analysis of Random Coefficient Autoregressive models
... Through a Monte Carlo simulation study, we evaluated the performance of the Maximum Likelihood estimation procedure and showed that the procedure works reasonably well for data of small to moderate sample sizes. The ...
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Variable selection in generalized random coefficient autoregressive models
... Quinn’s criterion [15], Geweke and Meese’s criterion [16], Cavanaugh’s Kullback informa- tion criterion [17], and the deviance information criterion of Spiegelhalter et al. [18]. Also, Tsay [19], Hurvich and Tsai [20] ...
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The First Order Autoregressive Model with Coefficient Contains Non Negative Random Elements: Simulation and Esimation
... [3] A. Aue, L. Horvath and J. Steinbach, “Estimation in Random Coefficient Autoregressive Models,” Jour- nal of Time Series Analysis, Vol. 27, No. 1, 2006, pp. 61-76. ...
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Fixed and random coefficient time series
... the models which have been considered previously with the random coefficient autoregressive model, since B^(t) , B^(t) and e(t) are not estimable ...various models using only the first ...
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Subject specific and population average models for binary longitudinal data: a tutorial
... The models fitted are the simple logistic regression, the PA with independent correlation matrix (Ind), the SS with random intercepts (RI), the PA with exchangeable (Exch), first-order autoregressive ...
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Statistical inference for first order random coefficient integer valued autoregressive processes
... integer-valued models have been studied by many authors (see, ...be random, Zheng et ...first-order random coefficient integer- valued autoregressive (RCINAR()) ...
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Statistical inference for the new INAR(2) models with random coefficient
... integer-valued autoregressive models (INARs) defined through the thinning operator are the most popular model for describing such count data and have been extensively investigated by McKenzie [1], and ...
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Test for parameter changes in generalized random coefficient autoregressive model
... series models (see, ...regression models with nonstationary ...an autoregressive time series ...first-order random coefficient integer-valued autoregressive ...series models and ...
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Estimation for random coefficient integer valued autoregressive model under random environment
... of random environment in the INAR models has greatly improved the adaptability of the ...integer-valued autoregressive models based on the negative binomial thinning opera- tor with different ...
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Option Pricing Applications of Quadratic Volatility Models
... volatility models. Various GARCH-type models have been developed and successfully applied in empirical ...theoretical models match stylized facts such as fat tails in most financial ...of ...
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Causes and effects of negative definite covariance matrices in Swamy Type Random Coefficient Models
... with coefficient heterogeneity are the Mean Group estimation, proposed by Pesaran and Smith (1995), and the Swamy (1970) random coefficient ...latter models the regression coefficients as ...
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Noncausal autoregressions for economic time series
... noncausal autoregressive models and their potential usefulness in economic ...these models, future errors are predictable, indicating that they can be used to empirically approach rational expec- ...
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Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties
... data) models, the pooled least squares (classical pooling) estimator is the best linear unbiased estimator (BLUE) under the classical assumptions as in the general linear regression ...data models is that ...
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On nonergodicity for nonparametric autoregressive models
... The remainder of the paper is organized as follows. Section introduces the nonpara- metric autoregressive model with random time delay under random environment. Sec- tion develops some useful ...
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Autoregressive conditionally heteroscedastic models
... (4) fixed and bounded, and (b) the lagged values of the dependent variable, with autoregressive conditionally heteroscedastic (ARCH) errors are considered.. A series representation and s[r] ...
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Generalized Conditionally Autoregressive Models
... generalized mixed model is using with DCAR as random spatial effects, analytical exploration of the posterior distribution becomes almost prohibitive. Thus, the Gaus- sian DCAR model leads to intractable posterior ...
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Study & Development of Short Term Load Forecasting Models Using Stochastic Time Series Analysis
... series models rely on the analysis of the autocorrelation function (acf) and partial autocorrelation function ...an Autoregressive process, partial autocorrelation function (pacf) is useful in determination ...
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An autoregressive point source model for spatial process
... Stochastic processes across spatial domains are often modeled by decomposing them into trend and stationary error processes. However, it has become increasingly clear that an assumption of stationarity of the error ...
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Compare Value at Risk and Return of Assets Portfolio Stock, Gold, REIT, U.S. & Iran Market Indices
... This paper surveys risk and return of assets portfolio that refer to combination of Stock, Real state and Gold because people often tent to investing on this three assets. .Here we compare portfolios of this three assets ...
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