The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is...

The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is...

In this paper we employ the Kullback Information apparatus in (a) obtaining the strong consistency of the maximum likelihood (ML) estimator in the standard version of the general linear structural econometric model (GLSEM); (b) deriving very succintly the necessary and sufficient (nas)...

This paper proposes a range of parametric and nonparametric corrections to the GPH estimator of the differencing parameter in a fractionally integrated process. It is argued that the GPH estimator can suffer from an identification problem at the very low frequencies in the spectrum in small samples.

In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way error components regression model suitable for incomplete panel and including parametrically specified variance functions for both individual-specific and general error disturbances.

In this paper, I first prove an integral representation theorem: Every quasi-integral on a Stone lattice can be represented by a unique upper-continuous capacity. I then apply this representation theorem to study the topological structure of the space of all upper-continuous capacities on a...

The standard version of "q" theory, in which investment is positively related to marginal "q", breaks down in the presence of fixed costs of adjustment. With fixed costs, q is a non-monotonic function of investment. Therefore its inverse, which is the traditional investment function, does not...

We prove that the recently proposed informational herding models are but special cases of a standard single person experimentation model with myopia. We then re-interpret the incorrect herding outcome as a familiar failure of complete learning in an optimal experimentation problem.