Estimation for Autoregressive Processes with Unit Roots

Testing for Seasonal Unit Roots in Periodic Integrated Autoregressive Processes

Parameter estimation for non-Gaussian autoregressive processes

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...

On periodic autoregressive processes estimation

We consider the autoregressive estimation for periodically correlated processes, using the parameterization given by the partial autocorrelation function. We propose an estimation of these parameters by extending the sample partial autocorrelation method to this situation. The comparison with other methods is made. Relationships with the stationary multivariate case are discussed.

GMM Estimation of Autoregressive Roots Near Unity with Panel Data

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic trends. Such models arise in empirical econometric studies of Þrm size and in dynamic panel data modeling with weak instruments. The two moment conditions in the GMM approach are obtained by constructing bias corrections to the ...

Jackknife Estimation in Autoregressive Models with a Unit Root∗

This paper examines the performance of the jackknife resampling method in unit root autoregression. Two different sub-sampling methods used for deriving jackknife estimators are considered: the first one employs non-overlapping subgroups, while the second one uses moving blocks of observations. In the case of a pure random walk, from the second subgroup onwards, the associated initial condition...