Root cancellation in Auto Regressive Moving Average (ARMA) models leads tolocal non-identification of parameters. When we use diffuse or normal priorson the parameters of the ARMA model, posteriors in Bayesian analyzes show ana posteriori favor for this local non-identification. We show that the...

Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters.<br>...

In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to...

We propose in this paper a likelihood-based framework for cointegration analysis in panels of a fixed number of vector error correction models. Maximum likelihood estimators of the cointegrating vectors are constructed using iterated Generalized Method of Moments estimators. Using these...

We construct a novel statistic to test hypothezes on subsets of the structural parameters in an Instrumental Variables (IV) regression model. We derive the chi squared limiting distribution of the statistic and show that it has a degrees of freedom parameter that is equal to the number of...

We show that three convenient statistical properties that are known to hold for the linear model with normal distributed errors that: (i.) when the variance is known, the likelihood based test statistics, Wald, Likelihood Ratio and Score or Lagrange Multiplier, coincide, (ii.) when the variance...

Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying...