Saturation in autoregressive models

Statistical Inference in Autoregressive Models with Non-negative Residuals

Normal residual is one of the usual assumptions of autoregressive models but in practice sometimes we are faced with non-negative residuals case. In this paper we consider some autoregressive models with non-negative residuals as competing models and we have derived the maximum likelihood estimators of parameters based on the modified approach and EM algorithm for the competing models. Also,...

Bias reduction in autoregressive models

It is well known that standard estimators of an AR(p) model are biased in finite samples, yet little is done in practice to remove the bias. Apparently small biases have important implications for the estimation of the impulse response function, which is a nonlinear function of the original coefficients. This note shows how to obtain estimators adjusted for first order bias based on Stine and S...

Bivariate binomial autoregressive models

This paper introduces new classes of bivariate time series models being useful to fit count data time series with a finite range of counts. Motivation comes mainly from the comparison of schemes for monitoring tourism demand, stock data, production and environmental processes. All models are based on the bivariate binomial distribution of Type II. First, a new family of bivariate integer-valued...

Vector Autoregressive Models

Inverse Gaussian Autoregressive Models

A first-order autoregressive process with inverse gaussian marginals is introduced. The innovation distributions are obtained under certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. The behavior of the estimators for small samples is studied through simulation experiments. On Sums of Tri...